Awesome-3D-Visual-Grounding icon indicating copy to clipboard operation
Awesome-3D-Visual-Grounding copied to clipboard

verified publisher icon liudaizong
→

Metadata

😎 up-to-date & curated list of awesome 3D Visual Grounding papers, methods & resources.

Awesome-3D-Visual-Grounding Awesome

A continual collection of papers related to Text-guided 3D Visual Grounding (T-3DVG).

Text-guided 3D visual grounding (T-3DVG) aims to locate a specific object that semantically corresponds to a language query from a complicated 3D scene, has drawn increasing attention in the 3D research community over the past few years. T-3DVG presents great potential and challenges due to its closer proximity to the real world and the complexity of data collection and 3D point cloud source processing.

In the T-3DVG community, we've summarized existing T-3DVG methods in our survey paper👍.

A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions.

If you find some important work missed, it would be super helpful to let me know ([email protected]). Thanks!

If you find our survey useful for your research, please consider citing:

@article{liu2024survey,
  title={A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions},
  author={Liu, Daizong and Liu, Yang and Huang, Wencan and Hu, Wei},
  journal={arXiv preprint arXiv:2406.05785},
  year={2024}
}

Table of Contents

  • Fully-supervised two-stage approach
  • Fully-supervised one-stage approach
  • Weakly-supervised approach
  • Semi-supervised approach
  • Approaches using No Point Cloud Input
  • Approaches using LLMs
  • Approaches tackling Outdoor Scenes

Fully-Supervised-Two-Stage

  • ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language | Github
    • Dave Zhenyu Chen, Angel X. Chang, Matthias Nießner
    • Technical University of Munich, Simon Fraser University
    • [ECCV2020] https://arxiv.org/abs/1912.08830
    • A dataset, two-stage approach, proposal-then-selection
  • ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes | Github
    • Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, Leonidas Guibas
    • Stanford University, King Abdullah University of Science and Technology
    • [ECCV2020] https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460409.pdf
    • A dataset, two-stage approach, proposal-then-selection
  • Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud | Github
    • Mingtao Feng, Zhen Li, Qi Li, Liang Zhang, XiangDong Zhang, Guangming Zhu, Hui Zhang, Yaonan Wang, Ajmal Mian
    • Xidian University, Hunan University, The University of Western Australia
    • [ICCV2021] https://openaccess.thecvf.com/content/ICCV2021/papers/Feng_Free-Form_Description_Guided_3D_Visual_Graph_Network_for_Object_Grounding_ICCV_2021_paper.pdf
    • Two-stage approach, proposal-then-selection, graph neural network
  • InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring | Github
    • Zhihao Yuan, Xu Yan, Yinghong Liao, Ruimao Zhang, Sheng Wang, Zhen Li, Shuguang Cui
    • The Chinese University of Hong Kong (Shenzhen), Southern University of Science and Technology
    • [ICCV2021] https://openaccess.thecvf.com/content/ICCV2021/papers/Yuan_InstanceRefer_Cooperative_Holistic_Understanding_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.pdf
    • Two-stage approach, proposal-then-selection, segmentation
  • SAT: 2D Semantics Assisted Training for 3D Visual Grounding | Github
    • Zhengyuan Yang, Songyang Zhang, Liwei Wang, Jiebo Luo
    • University of Rochester, The Chinese University of Hong Kong
    • [ICCV2021] https://arxiv.org/pdf/2105.11450
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • Text-Guided Graph Neural Networks for Referring 3D Instance Segmentation | Github
    • Pin-Hao Huang, Han-Hung Lee, Hwann-Tzong Chen, Tyng-Luh Liu
    • Academia Sinica, National Tsing Hua University, Taiwan AI Labs, Aeolus Robotics
    • [AAAI2021] https://ojs.aaai.org/index.php/AAAI/article/view/16253
    • Two-stage approach, proposal-then-selection, segmentation
  • LanguageRefer: Spatial-Language Model for 3D Visual Grounding | Github
    • Junha Roh, Karthik Desingh, Ali Farhadi, Dieter Fox
    • University of Washington
    • [CoRL2021] https://openreview.net/pdf?id=dgQdvPZnH-t
    • Two-stage approach, proposal-then-selection, spatial embedding, positional encoding
  • TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding | Github
    • Dailan He, Yusheng Zhao, Junyu Luo, Tianrui Hui, Shaofei Huang, Aixi Zhang, Si Liu
    • Beihang University, Chinese Academy of Sciences, Alibaba Group
    • [ACMMM2021] https://arxiv.org/abs/2108.02388
    • Two-stage approach, proposal-then-selection, entity-and-relation
  • 3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds | Github
    • Daigang Cai, Lichen Zhao, Jing Zhang, Lu Sheng, Dong Xu
    • Beihang University, The University of Sydney
    • [CVPR2022] https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.pdf
    • Two-stage approach, proposal-then-selection, joint 3D captioning and grounding
  • Multi-View Transformer for 3D Visual Grounding | Github
    • Shijia Huang, Yilun Chen, Jiaya Jia, Liwei Wang
    • The Chinese University of Hong Kong
    • [CVPR2022] https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Multi-View_Transformer_for_3D_Visual_Grounding_CVPR_2022_paper.pdf
    • Two-stage approach, proposal-then-selection, additional multi-view input
  • 3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language |
    • Ahmed Abdelreheem, Ujjwal Upadhyay, Ivan Skorokhodov, Rawan Al Yahya, Jun Chen, Mohamed Elhoseiny
    • King Abdullah University of Science and Technology
    • [WACV2022] https://openaccess.thecvf.com/content/WACV2022/papers/Abdelreheem_3DRefTransformer_Fine-Grained_Object_Identification_in_Real-World_Scenes_Using_Natural_Language_WACV_2022_paper.pdf
    • Two-stage approach, proposal-then-selection
  • D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding | Github
    • Dave Zhenyu Chen, Qirui Wu, Matthias Nießner, Angel X. Chang
    • Technical University of Munich, Simon Fraser University
    • [ECCV2022] https://arxiv.org/abs/2112.01551
    • Two-stage approach, proposal-then-selection, joint 3D captioning and grounding
  • Language Conditioned Spatial Relation Reasoning for 3D Object Grounding | Github
    • Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid, Ivan Laptev
    • PSL Research University, IIIT Hyderabad
    • [NeurIPS2022] https://arxiv.org/abs/2211.09646
    • Two-stage approach, proposal-then-selection, spatial relation
  • Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding | Github
    • Eslam Mohamed Bakr, Yasmeen Alsaedy, Mohamed Elhoseiny
    • King Abdullah University of Science and Technology
    • [NeurIPS2022] https://arxiv.org/abs/2211.14241
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • Context-aware Alignment and Mutual Masking for 3D-Language Pre-training | Github
    • Zhao Jin, Munawar Hayat, Yuwei Yang, Yulan Guo, Yinjie Lei
    • Sichuan University, Monash University, Sun Yat-sen University
    • [CVPR2023] https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Context-Aware_Alignment_and_Mutual_Masking_for_3D-Language_Pre-Training_CVPR_2023_paper.pdf
    • Two-stage approach, proposal-then-selection, pre-training
  • NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations | Github
    • Joy Hsu, Jiayuan Mao, Jiajun Wu
    • Stanford University, Massachusetts Institute of Technology
    • [CVPR2023] https://arxiv.org/abs/2303.13483
    • Two-stage approach, proposal-then-selection, semantic learning
  • 3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment | Github
    • Ziyu Zhu, Xiaojian Ma, Yixin Chen, Zhidong Deng, Siyuan Huang, Qing Li
    • Tsinghua University, BIGAI
    • [ICCV2023] https://arxiv.org/pdf/2308.04352
    • Two-stage approach, proposal-then-selection, pre-training
  • Multi3DRefer: Grounding Text Description to Multiple 3D Objects | Github
    • Yiming Zhang, ZeMing Gong, Angel X. Chang
    • Simon Fraser University, Alberta Machine Intelligence Institute
    • [ICCV2023] https://3dlg-hcvc.github.io/multi3drefer/
    • A dataset, Two-stage approach, proposal-then-selection, multiple-object grounding
  • UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding |
    • Dave Zhenyu Chen, Ronghang Hu, Xinlei Chen, Matthias Nießner, Angel X. Chang
    • Technical University of Munich, Meta AI, Simon Fraser University
    • [ICCV2023] https://arxiv.org/abs/2212.00836
    • Two-stage approach, proposal-then-selection, joint 3D captioning and grounding
  • ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance | Github
    • Zoey Guo, Yiwen Tang, Ray Zhang, Dong Wang, Zhigang Wang, Bin Zhao, Xuelong Li
    • Shanghai Artificial Intelligence Laboratory, The Chinese University of Hong Kong, Northwestern Polytechnical University
    • [ICCV2023] https://arxiv.org/pdf/2303.16894
    • Two-stage approach, proposal-then-selection, additional multi-view input
  • ARKitSceneRefer: Text-based Localization of Small Objects in Diverse Real-World 3D Indoor Scenes | Github
    • Shunya Kato, Shuhei Kurita, Chenhui Chu, Sadao Kurohashi
    • Kyoto University, RIKEN
    • [EMNLP2023] https://aclanthology.org/2023.findings-emnlp.56.pdf
    • A dataset, Two-stage approach, proposal-then-selection, small object
  • Reimagining 3D Visual Grounding: Instance Segmentation and Transformers for Fragmented Point Cloud Scenarios |
    • Zehan Tan, Weidong Yang, Zhiwei Wang
    • Fudan University, GREE ELECTRIC APPLIANCES
    • [ACMMMAsia2023] https://dl.acm.org/doi/10.1145/3595916.3626405
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • HAM: Hierarchical Attention Model with High Performance for 3D Visual Grounding |
    • Jiaming Chen, Weixin Luo, Xiaolin Wei, Lin Ma, and Wei Zhang
    • Shandong University, Meituan
    • [Arxiv2023] https://arxiv.org/abs/2210.12513
    • One-stage approach, proposal-then-selection, point detection
  • Three Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding |
    • Ozan Unal, Christos Sakaridis, Suman Saha, Fisher Yu, Luc Van Gool
    • ETH Zurich
    • [Arxiv2023] https://arxiv.org/abs/2309.04561
    • Two-stage approach, proposal-then-selection, segmentation
  • ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding |
    • Ziyang Lu, Yunqiang Pei, Guoqing Wang, Yang Yang, Zheng Wang, Heng Tao Shen
    • University of Electronic Science and Technology of China
    • [Arxiv2023] https://arxiv.org/abs/2303.13186
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes | Github
    • Ahmed Abdelreheem, Kyle Olszewski, Hsin-Ying Lee, Peter Wonka, Panos Achlioptas
    • King Abdullah University of Science and Technology, Snap Inc.
    • [WACV2024] https://openaccess.thecvf.com/content/WACV2024/papers/Abdelreheem_ScanEnts3D_Exploiting_Phrase-to-3D-Object_Correspondences_for_Improved_Visio-Linguistic_Models_in_3D_WACV_2024_paper.pdf
    • Two-stage approach, proposal-then-selection
  • COT3DREF: Chain-of-Thoughts Data-Efficeint 3D Visual Grounding | Github
    • Eslam Mohamed Bakr, Mohamed Ayman, Mahmoud Ahmed, Habib Slim, Mohamed Elhoseiny
    • King Abdullah University of Science and Technology
    • [ICLR2024] https://arxiv.org/abs/2310.06214
    • Two-stage approach, proposal-then-selection, Chain-of-Thoughts
  • Exploiting Contextual Objects and Relations for 3D Visual Grounding | Github
    • Li Yang, Chunfeng Yuan, Ziqi Zhang, Zhongang Qi, Yan Xu, Wei Liu, Ying Shan, Bing Li, Weiping Yang, Peng Li, Yan Wang, Weiming Hu
    • CASIA, Tencent PCG, The Chinese University of Hong Kong, Ministry of Education, Alibaba Group, Zhejiang Linkheer Science And Technology Co., Ltd., University of Chinese Academy of Sciences, ShanghaiTech University
    • [NeurIPS2024] https://papers.nips.cc/paper_files/paper/2023/hash/9b91ee0da3bcd61905fcd89e770168fc-Abstract-Conference.html
    • Two-stage approach, proposal-then-selection
  • Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D Scans | Github
    • Taiki Miyanishi, Daichi Azuma, Shuhei Kurita, Motoaki Kawanabe
    • ATR, Kyoto University, RIKEN AIP
    • [3DV2024] https://arxiv.org/abs/2305.13876
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • A Transformer-based Framework for Visual Grounding on 3D Point Clouds |
    • Ali Solgi, Mehdi Ezoji
    • Babol Noshirvani University of Technology
    • [AISP2024] https://ieeexplore.ieee.org/abstract/document/10475280/
    • Two-stage approach, proposal-then-selection
  • MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding |
    • Chun-Peng Chang, Shaoxiang Wang, Alain Pagani, Didier Stricker
    • DFKI Augmented Vision
    • [CVPR2024] https://arxiv.org/abs/2403.03077
    • Two-stage approach, proposal-then-selection, spatial relation
  • Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and Consistency | Github
    • Yuqi Zhang, Han Luo, Yinjie Lei
    • Sichuan University
    • [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Towards_CLIP-driven_Language-free_3D_Visual_Grounding_via_2D-3D_Relational_Enhancement_CVPR_2024_paper.pdf
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • Multi-Attribute Interactions Matter for 3D Visual Grounding | Github
    • Can Xu, Yuehui Han, Rui Xu, Le Hui, Jin Xie, Jian Yang
    • Nanjing University of Science and Technology, Northwestern Polytechnical University, Nanjing University
    • [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Multi-Attribute_Interactions_Matter_for_3D_Visual_Grounding_CVPR_2024_paper.pdf
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • Viewpoint-Aware Visual Grounding in 3D Scenes |
    • Xiangxi Shi, Zhonghua Wu, Stefan Lee
    • Oregon State University, SenseTime Research
    • [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_Viewpoint-Aware_Visual_Grounding_in_3D_Scenes_CVPR_2024_paper.pdf
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • Advancing 3D Object Grounding Beyond a Single 3D Scene |
    • Wencan Huang, Daizong Liu, Wei Hu
    • Peking University
    • [ACMMM2024] https://openreview.net/forum?id=KuHaYRftVN
    • Two-stage approach, proposal-then-selection, flexible-object group-wise scene grounding
  • Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention |
    • Haomeng Zhang, Chiao-An Yang, Raymond A. Yeh
    • Purdue University
    • [NeurIPS2024] https://arxiv.org/abs/2410.22306
    • Two-stage approach, proposal-then-selection, multi-object grounding
  • Fine-Grained Spatial and Verbal Losses for 3D Visual Grounding |
    • Sombit Dey, Ozan Unal, Christos Sakaridis, Luc Van Gool
    • ETH Zurich, INSAIT, Huawei Technologies, KU Leuven
    • [WACV2024] https://arxiv.org/abs/2411.03405
    • Two-stage approach, novel losses
  • SCENEVERSE: Scaling 3D Vision-Language Learning for Grounded Scene Understanding | Github
    • Baoxiong Jia , Yixin Chen , Huanyue Yu, Yan Wang, Xuesong Niu, Tengyu Liu, Qing Li, Siyuan Huang
    • Beijing Institute for General Artificial Intelligence
    • [Arxiv2024] https://arxiv.org/abs/2401.09340
    • A dataset, Two-stage approach, proposal-then-selection
  • SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention | Github
    • Feng Xiao, Hongbin Xu, Qiuxia Wu, Wenxiong Kang
    • South China University of Technology
    • [Arxiv2024] https://arxiv.org/abs/2403.08182
    • Two-stage approach, proposal-then-selection, additional multi-modal input
  • DOrA: 3D Visual Grounding with Order-Aware Referring |
    • Tung-Yu Wu, Sheng-Yu Huang, Yu-Chiang Frank Wang
    • National Taiwan University, NVIDIA
    • [Arxiv2024] https://arxiv.org/abs/2403.16539
    • Two-stage approach, proposal-then-selection, Chain-of-Thoughts
  • Dual Attribute-Spatial Relation Alignment for 3D Visual Grounding |
    • Yue Xu, Kaizhi Yang, Jiebo Luo, Xuejin Chen
    • University of Science and Technology of China, University of Rochester
    • [Arxiv2024] https://arxiv.org/abs/2406.08907
    • Two-stage approach, proposal-then-selection, spatial embedding
  • R2G: Reasoning to Ground in 3D Scenes |
    • Yixuan Li, Zan Wang, Wei Liang
    • n/a
    • [Arxiv2024] https://arxiv.org/abs/2408.13499
    • Two-stage approach, proposal-then-selection, scene graph reasoning
  • AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring |
    • Xinyi Wang, Na Zhao, Zhiyuan Han, Dan Guo, Xun Yang
    • University of Science and Technology of China, Singapore University of Technology and Design, Hefei University of Technology
    • [AAAI2025] https://arxiv.org/abs/2501.09428
    • Two-stage approach, object injection/augmentation
  • DSM: Building A Diverse Semantic Map for 3D Visual Grounding |
    • Qinghongbing Xie, Zijian Liang, Long Zeng
    • Tsinghua University, South China University of Technology
    • [Arxiv2025] https://arxiv.org/abs/2504.08307
    • Two-stage approach, multi-view information, semantic map #
  • AS3D: 2D-Assisted Cross-Modal Understanding with Semantic-Spatial Scene Graphs for 3D Visual Grounding | Github
    • Feng Xiao, Hongbin Xu, Guocan Zhao, Wenxiong Kang
    • South China University of Technology
    • [Arxiv2025] https://arxiv.org/abs/2505.04058
    • Two-stage approach, 2D-3D, segmentation #
  • Ges3ViG: Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference Understanding | Github
    • Atharv Mahesh Mane, Dulanga Weerakoon, Vigneshwaran Subbaraju, Sougata Sen, Sanjay E. Sarma, Archan Misra
    • Stony Brook University, BITS Pilani Goa campus, Singapore-MIT Alliance for Research and Technology Centre
    • [CVPR2025] https://arxiv.org/abs/2504.09623
    • Two-stage approach, gesture #

Fully-Supervised-One-Stage

  • 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds | Github
    • Lichen Zhao, Daigang Cai, Lu Sheng, Dong Xu
    • Beihang University, The University of Sydney
    • [ICCV2021] https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.pdf
    • One-stage approach, unified detection-interaction
  • 3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection | Github
    • Junyu Luo, Jiahui Fu, Xianghao Kong, Chen Gao, Haibing Ren, Hao Shen, Huaxia Xia, Si Liu
    • Beihang University, Meituan Inc.
    • [CVPR2022] https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_3D-SPS_Single-Stage_3D_Visual_Grounding_via_Referred_Point_Progressive_Selection_CVPR_2022_paper.pdf
    • One-stage approach, regression-based
  • Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds | Github
    • Ayush Jain, Nikolaos Gkanatsios, Ishita Mediratta, Katerina Fragkiadaki
    • Carnegie Mellon University, Meta AI
    • [ECCV2022] https://arxiv.org/abs/2112.08879
    • One-stage approach, unified detection-interaction
  • EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding | Github
    • Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, Jian Zhang
    • Peking University, The Chinese University of Hong Kong, Peng Cheng Laboratory, Shanghai AI Laboratory
    • [CVPR2023] https://arxiv.org/abs/2209.14941
    • One-stage approach, unified detection-interaction, text-decoupling, dense
  • Dense Object Grounding in 3D Scenes |
    • Wencan Huang, Daizong Liu, Wei Hu
    • Peking University
    • [ACMMM2023] https://arxiv.org/abs/2309.02224
    • One-stage approach, unified detection-interaction, transformer
  • 3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding |
    • Zehan Wang, Haifeng Huang, Yang Zhao, Linjun Li, Xize Cheng, Yichen Zhu, Aoxiong Yin, Zhou Zhao
    • Zhejiang University, ByteDance
    • [EMNLP2023] https://aclanthology.org/2023.emnlp-main.656/
    • One-stage approach, unified detection-interaction, relative position
  • LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark | Github
    • Zhenfei Yin, Jiong Wang, Jianjian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, Jing Shao, Wanli Ouyang
    • Shanghai AI Lab, Beihang University, The Chinese University of Hong Kong (Shenzhen), Fudan University, Dalian University of Technology, The University of Sydney
    • [NeurIPs2023] https://arxiv.org/abs/2306.06687
    • A dataset, One-stage approach, regression-based, multi-task
  • PATRON: Perspective-Aware Multitask Model for Referring Expression Grounding Using Embodied Multimodal Cues |
    • Md Mofijul Islam, Alexi Gladstone, Tariq Iqbal
    • University of Virginia
    • [AAAI2023] https://ojs.aaai.org/index.php/AAAI/article/view/25177
    • A dataset, One-stage approach, multi-task, multi-modal
  • Toward Fine-Grained 3D Visual Grounding through Referring Textual Phrases | Github
    • Zhihao Yuan, Xu Yan, Zhuo Li, Xuhao Li, Yao Guo, Shuguang Cui, Zhen Li
    • CUHK-Shenzhen, Shanghai Jiao Tong University
    • [Arxiv2023] https://arxiv.org/abs/2207.01821
    • A dataset, One-stage approach, unified detection-interaction
  • A Unified Framework for 3D Point Cloud Visual Grounding | Github
    • Haojia Lin, Yongdong Luo, Xiawu Zheng, Lijiang Li, Fei Chao, Taisong Jin, Donghao Luo, Yan Wang, Liujuan Cao, Rongrong Ji
    • Xiamen University, Peng Cheng Laboratory
    • [Arxiv2023] https://arxiv.org/abs/2308.11887
    • One-stage approach, unified detection-interaction, superpoint
  • Uni3DL: Unified Model for 3D and Language Understanding |
    • Xiang Li, Jian Ding, Zhaoyang Chen, Mohamed Elhoseiny
    • King Abdullah University of Science and Technology, Ecole Polytechnique
    • [Arxiv2023] https://arxiv.org/abs/2312.03026
    • One-stage approach, regression-based, multi-task
  • 3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation | Github
    • Changli Wu, Yiwei Ma, Qi Chen, Haowei Wang, Gen Luo, Jiayi Ji, Xiaoshuai Sun
    • Xiamen University
    • [AAAI2024] https://arxiv.org/abs/2308.16632
    • One-stage approach, unified detection-interaction, superpoint
  • Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding |
    • Taolin Zhang, Sunan He, Tao Dai, Zhi Wang, Bin Chen, Shu-Tao Xia
    • Tsinghua University, Hong Kong University of Science and Technology, Shenzhen University, Harbin Institute of Technology(Shenzhen), Peng Cheng Laboratory
    • [AAAI2024] https://arxiv.org/abs/2305.10714
    • One-stage approach, regression-based, pre-training
  • Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding | Github
    • Zhihao Yuan, Jinke Ren, Chun-Mei Feng, Hengshuang Zhao, Shuguang Cui, Zhen Li
    • The Chinese University of Hong Kong (Shenzhen), A*STAR, The University of Hong Kong
    • [CVPR2024] https://arxiv.org/abs/2311.15383
    • One-stage approach, zero-shot, data construction
  • G3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding |
    • Yuan Wang, Yali Li, Shengjin Wang
    • Tsinghua University, Beijing National Research Center for Information Science and Technology
    • [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_G3-LQ_Marrying_Hyperbolic_Alignment_with_Explicit_Semantic-Geometric_Modeling_for_3D_CVPR_2024_paper.pdf
    • One-stage approach, regression-based
  • PointCloud-Text Matching: Benchmark Datasets and a Baseline |
    • Yanglin Feng, Yang Qin, Dezhong Peng, Hongyuan Zhu, Xi Peng, Peng Hu
    • Sichuan University, A*STAR
    • [Arxiv2024] https://arxiv.org/abs/2403.19386
    • A dataset, One-stage approach, regression-based, pre-training
  • PD-TPE: Parallel Decoder with Text-guided Position Encoding for 3D Visual Grounding |
    • Chenshu Hou, Liang Peng, Xiaopei Wu, Wenxiao Wang, Xiaofei He
    • Zhejiang University, FABU Inc.
    • [Arxiv2024] https://arxiv.org/abs/2407.14491
    • A dataset, One-stage approach
  • Grounding 3D Scene Affordance From Egocentric Interactions |
    • Cuiyu Liu, Wei Zhai, Yuhang Yang, Hongchen Luo, Sen Liang, Yang Cao, Zheng-Jun Zha
    • University of Science and Technology of China, Northeastern University
    • [Arxiv2024] https://arxiv.org/abs/2409.19650
    • A dataset, One-stage approach, video
  • Multi-branch Collaborative Learning Network for 3D Visual Grounding | Github
    • Zhipeng Qian, Yiwei Ma, Zhekai Lin, Jiayi Ji, Xiawu Zheng, Xiaoshuai Sun, Rongrong Ji
    • Xiamen University
    • [ECCV2024] https://arxiv.org/abs/2407.05363
    • One-stage approach, regression-based
  • Joint Top-Down and Bottom-Up Frameworks for 3D Visual Grounding |
    • Yang Liu, Daizong Liu, Wei Hu
    • Peking University
    • [ICPR2024] https://arxiv.org/abs/2410.15615
    • Joint top-down and bottom-up, One-stage approach
  • ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding |
    • Qihang Peng, Henry Zheng, Gao Huang
    • Tsinghua University
    • [ArXiv2025] https://arxiv.org/abs/2502.19247
    • One-stage approach, proxy attention, 2D image, unified clustering-interaction #
  • Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding | Github
    • Wenxuan Guo, Xiuwei Xu, Ziwei Wang, Jianjiang Feng, Jie Zhou, Jiwen Lu
    • Tsinghua University, Nanyang Technological University
    • [CVPR2025] https://arxiv.org/abs/2502.10392
    • One-stage approach, sparse voxel pruning, efficient

Weakly-supervised

  • Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding | Github
    • Zehan Wang, Haifeng Huang, Yang Zhao, Linjun Li, Xize Cheng, Yichen Zhu, Aoxiong Yin, Zhou Zhao
    • Zhejiang University
    • [ICCV2023] https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_Distilling_Coarse-to-Fine_Semantic_Matching_Knowledge_for_Weakly_Supervised_3D_Visual_ICCV_2023_paper.pdf
    • weakly-supervised, two-stage matching, pseudo label
  • Weakly-Supervised 3D Visual Grounding based on Visual Linguistic Alignment |
    • Xiaoxu Xu, Yitian Yuan, Qiudan Zhang, Wenhui Wu, Zequn Jie, Lin Ma, Xu Wang
    • Meituan
    • [Arxiv2023] https://arxiv.org/abs/2312.09625
    • weakly-supervised, two-stage matching, multi-modal
  • Weakly-supervised 3D Referring Expression Segmentation |
    • Yihang Liu, Changli Wu, Xiaoshuai Sun, Jiayi Ji, Yiwei Ma, Gen Luo, Liujuan Cao, Rongrong Ji
    • Xiamen University
    • [Openreview2024] https://openreview.net/forum?id=cSAAGL0cn0
    • Weakly-supervised, two-stage matching, segmentation-then-matching
  • 3DWG: 3D Weakly Supervised Visual Grounding via Category and Instance-Level Alignment |
    • Xiaoqi Li, Jiaming Liu, Nuowei Han, Liang Heng, Yandong Guo, Hao Dong, Yang Liu
    • Peking University
    • [ICRA2025] https://arxiv.org/abs/2505.01809
    • Weakly-supervised #

Semi-supervised

  • Cross-Task Knowledge Transfer for Semi-supervised Joint 3D Grounding and Captioning |
    • Yang Liu, Daizong Liu, Zongming Guo, Wei Hu
    • Peking University
    • [ACMMM2024] https://openreview.net/forum?id=OmymuhXWcn
    • semi-supervised, cross-task teacher-student framework, joint 3D grounding and captioning
  • Bayesian Self-Training for Semi-Supervised 3D Segmentation |
    • Ozan Unal, Christos Sakaridis, Luc Van Gool
    • ETH Zurich, Huawei Technologies, KU Leuven, INSAIT
    • [ECCV2024] https://arxiv.org/abs/2409.08102
    • semi-supervised, self-training

Other-Modality

  • Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images | Github
    • Haolin Liu, Anran Lin, Xiaoguang Han, Lei Yang, Yizhou Yu, Shuguang Cui
    • CUHK-Shenzhen, Deepwise AI Lab, The University of Hong Kong
    • [CVPR2021] https://arxiv.org/pdf/2103.07894
    • No point cloud input, RGB-D image
  • PATRON: Perspective-Aware Multitask Model for Referring Expression Grounding Using Embodied Multimodal Cues |
    • Md Mofijul Islam, Alexi Gladstone, Tariq Iqbal
    • University of Virginia
    • [AAAI2023] https://ojs.aaai.org/index.php/AAAI/article/view/25177
    • No point cloud input, multi-view
  • Mono3DVG: 3D Visual Grounding in Monocular Images | Github
    • Yang Zhan, Yuan Yuan, Zhitong Xiong
    • Northwestern Polytechnical University, Technical University of Munich
    • [AAAI2024] https://arxiv.org/pdf/2312.08022
    • No point cloud input, monocular image
  • EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI | Github
    • Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, Xihui Liu, Cewu Lu, Dahua Lin, Jiangmiao Pang
    • Shanghai AI Laboratory, Shanghai Jiao Tong University, The University of Hong Kong, The Chinese University of Hong Kong, Tsinghua University
    • [CVPR2024] https://arxiv.org/abs/2312.16170
    • A dataset, No point cloud input, RGB-D image
  • WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language |
    • Zhenxiang Lin, Xidong Peng, Peishan Cong, Yuenan Hou, Xinge Zhu, Sibei Yang, Yuexin Ma
    • ShanghaiTech University, Shanghai AI Laboratory, The Chinese University of Hong Kong
    • [Arxiv2023] https://arxiv.org/abs/2304.05645
    • No point cloud input, wild point cloud, additional multi-modal input
  • HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models |
    • Vineet Bhat, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
    • New York University
    • [Arxiv2024] https://arxiv.org/abs/2409.10419
    • No point cloud input, RGB image
  • ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation |
    • Yunhong Min, Daehyeon Choi, Kyeongmin Yeo, Jihyun Lee, Minhyuk Sung
    • KAIST
    • [Arxiv2025] https://arxiv.org/abs/2503.22194
    • No point cloud input, RGB image #

LLMs-based

  • ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance | Github
    • Zoey Guo, Yiwen Tang, Ray Zhang, Dong Wang, Zhigang Wang, Bin Zhao, Xuelong Li
    • Shanghai Artificial Intelligence Laboratory, The Chinese University of Hong Kong, Northwestern Polytechnical University
    • [ICCV2023] https://arxiv.org/pdf/2303.16894
    • LLMs-based, enriching text description
  • LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark | Github
    • Zhenfei Yin, Jiong Wang, Jianjian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, Jing Shao, Wanli Ouyang
    • Shanghai AI Lab, Beihang University, The Chinese University of Hong Kong (Shenzhen), Fudan University, Dalian University of Technology, The University of Sydney
    • [NeurIPs2023] https://arxiv.org/abs/2306.06687
    • LLMs-based, LLM architecture
  • Transcribe3D: Grounding LLMs Using Transcribed Information for 3D Referential Reasoning with Self-Corrected Finetuning |
    • Jiading Fang, Xiangshan Tan, Shengjie Lin, Hongyuan Mei, Matthew R. Walter
    • Toyota Technological Institute at Chicago
    • [CoRL2023] https://openreview.net/forum?id=7j3sdUZMTF
    • LLMs-based, enriching text description
  • LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent |
    • Jianing Yang, Xuweiyi Chen, Shengyi Qian, Nikhil Madaan, Madhavan Iyengar, David F. Fouhey, Joyce Chai1
    • University of Michigan, New York University
    • [Arxiv2023] https://arxiv.org/abs/2309.12311
    • LLMs-based, enriching text description
  • Mono3DVG: 3D Visual Grounding in Monocular Images | Github
    • Yang Zhan, Yuan Yuan, Zhitong Xiong
    • Northwestern Polytechnical University, Technical University of Munich
    • [AAAI2024] https://arxiv.org/pdf/2312.08022
    • LLMs-based, enriching text description
  • COT3DREF: Chain-of-Thoughts Data-Efficient 3D Visual Grounding | Github
    • Eslam Mohamed Bakr, Mohamed Ayman, Mahmoud Ahmed, Habib Slim, Mohamed Elhoseiny
    • King Abdullah University of Science and Technology
    • [ICLR2024] https://arxiv.org/abs/2310.06214
    • LLMs-based, Chain-of-Thoughts, reasoning
  • Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding | Github
    • Zhihao Yuan, Jinke Ren, Chun-Mei Feng, Hengshuang Zhao, Shuguang Cui, Zhen Li
    • The Chinese University of Hong Kong (Shenzhen), A*STAR, The University of Hong Kong
    • [CVPR2024] https://arxiv.org/abs/2311.15383
    • LLMs-based, construct text description
  • Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners | Github
    • Chun Feng, Joy Hsu, Weiyu Liu, Jiajun Wu
    • Stanford University
    • [CVPR2024] https://arxiv.org/abs/2404.19696
    • LLMs-based, language constraint
  • 3DMIT: 3D MULTI-MODAL INSTRUCTION TUNING FOR SCENE UNDERSTANDING | Github
    • Zeju Li, Chao Zhang, Xiaoyan Wang, Ruilong Ren, Yifan Xu, Ruifei Ma, Xiangde Liu
    • Beijing University of Posts and Telecommunications, Beijing Digital Native Digital City Research Center, Peking University, Beihang University, Beijing University of Science and Technology
    • [Arxiv2024] https://arxiv.org/abs/2401.03201
    • LLMs-based, LLM architecture
  • DOrA: 3D Visual Grounding with Order-Aware Referring |
    • Tung-Yu Wu, Sheng-Yu Huang, Yu-Chiang Frank Wang
    • National Taiwan University, NVIDIA
    • [Arxiv2024] https://arxiv.org/abs/2403.16539
    • LLMs-based, Chain-of-Thoughts
  • SCENEVERSE: Scaling 3D Vision-Language Learning for Grounded Scene Understanding | Github
    • Baoxiong Jia , Yixin Chen , Huanyue Yu, Yan Wang, Xuesong Niu, Tengyu Liu, Qing Li, Siyuan Huang
    • Beijing Institute for General Artificial Intelligence
    • [Arxiv2024] https://arxiv.org/abs/2401.09340
    • A dataset, LLMs-based, LLM architecture
  • Language-Image Models with 3D Understanding | Github
    • Jang Hyun Cho, Boris Ivanovic, Yulong Cao, Edward Schmerling, Yue Wang, Xinshuo Weng, Boyi Li, Yurong You, Philipp Krähenbühl, Yan Wang, Marco Pavone
    • UT Austin, NVIDIA Research
    • [Arxiv2024] https://arxiv.org/abs/2405.03685
    • A dataset, LLMs-based
  • Task-oriented Sequential Grounding in 3D Scenes | Github
    • Zhuofan Zhang, Ziyu Zhu, Pengxiang Li, Tengyu Liu, Xiaojian Ma, Yixin Chen, Baoxiong Jia, Siyuan Huang, Qing Li
    • BIGA, Tsinghua Universit, Beijing Institute of Technology
    • [Arxiv2024] https://arxiv.org/abs/2408.04034
    • A dataset, LLMs-based
  • Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding | Github
    • Yunze Man, Shuhong Zheng, Zhipeng Bao, Martial Hebert, Liang-Yan Gui, Yu-Xiong Wang
    • University of Illinois Urbana-Champaign, Carnegie Mellon University
    • [Arxiv2024] https://arxiv.org/abs/2409.03757
    • Foundation model
  • Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning |
    • Weitai Kang, Haifeng Huang, Yuzhang Shang, Mubarak Shah, Yan Yan
    • Illinois Institute of Technology, Zhejiang University, University of Central Florida, University of Illinois at Chicago
    • [Arxiv2024] https://arxiv.org/abs/2410.00255
    • LLMs-based
  • Solving Zero-Shot 3D Visual Grounding as Constraint Satisfaction Problems | Github
    • Qihao Yuan, Jiaming Zhang, Kailai Li, Rainer Stiefelhagen
    • Karlsruhe Institute of Technology, University of Groningen
    • [Arxiv2024] https://arxiv.org/abs/2411.14594
    • LLMs-based, zero-shot
  • SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding | Github
    • Rong Li, Shijie Li, Lingdong Kong, Xulei Yang, Junwei Liang
    • HKUST, A*STAR, National University of Singapore
    • [Arxiv2024] https://arxiv.org/abs/2412.04383
    • LLMs-based, zero-shot
  • Empowering 3D Visual Grounding with Reasoning Capabilities | Github
    • Chenming Zhu, Tai Wang, Wenwei Zhang, Kai Chen, Xihui Liu
    • The University of Hong Kong, Shanghai AI Laboratory
    • [ECCV2024] https://arxiv.org/abs/2407.01525
    • LLMs-based, LLM architecture, A dataset
  • VLM-Grounder: A VLM Agent for Zero-Shot 3D Visual Grounding | Github
    • Runsen Xu, Zhiwei Huang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin
    • The Chinese University of Hong Kong, Zhejiang University, Shanghai AI Laboratory, Centre for Perceptual and Interactive Intelligence
    • [CoRL2024] https://arxiv.org/abs/2410.13860
    • LLMs-based, zero-shot
  • ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding |
    • Austin T. Wang, ZeMing Gong, Angel X. Chang
    • Simon Fraser University, Alberta Machine Intelligence Institute
    • [Arxiv2025] https://arxiv.org/abs/2501.01366
    • LLMs-based, new dataset
  • Evolving Symbolic 3D Visual Grounder with Weakly Supervised Reflection | Github
    • Boyu Mi, Hanqing Wang, Tai Wang, Yilun Chen, Jiangmiao Pang
    • Shanghai AI Laboratory
    • [Arxiv2025] https://arxiv.org/abs/2502.01401
    • LLMs-based, training-free, advantages on accuracy and grounding cost
  • LIFT-GS: Cross-Scene Render-Supervised Distillation for 3D Language Grounding | Github
    • Ang Cao, Sergio Arnaud, Oleksandr Maksymets, Jianing Yang, Ayush Jain, Sriram Yenamandra, Ada Martin, Vincent-Pierre Berges, Paul McVay, Ruslan Partsey, Aravind Rajeswaran, Franziska Meier, Justin Johnson, Jeong Joon Park, Alexander Sax
    • University of Michigan, Meta, Carnegie Mellon University, Stanford University
    • [Arxiv2025] https://arxiv.org/abs/2502.20389
    • VLM-based, zero-shot #
  • 3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o |
    • Dingning Liu, Cheng Wang, Peng Gao, Renrui Zhang, Xinzhu Ma, Yuan Meng, Zhihui Wang
    • Shanghai AI Lab, Dalian University of Technology, Wuhan University, The Chinese University of Hong Kong, Tsinghua University
    • [Arxiv2025] https://arxiv.org/abs/2503.13185
    • MLLM-based, GPT-4o #
  • SORT3D: Spatial Object-centric Reasoning Toolbox for Zero-Shot 3D Grounding Using Large Language Models | Github
    • Nader Zantout, Haochen Zhang, Pujith Kachana, Jinkai Qiu, Ji Zhang, Wenshan Wang
    • Carnegie Mellon University
    • [Arxiv2025] https://arxiv.org/abs/2504.18684
    • LLMs-based, zero-shot #
  • LIBA: Language Instructed Multi-granularity Bridge Assistant for 3D Visual Grounding |
    • Yuan Wang, Ya-Li Li, W U Eastman Z Y, Shengjin Wang
    • Tsinghua University
    • [AAAI2025] https://ojs.aaai.org/index.php/AAAI/article/view/32875
    • LLM-based #
  • Grounding 3D Object Affordance with Language Instructions, Visual Observations and Interactions | Github
    • He Zhu, Quyu Kong, Kechun Xu, Xunlong Xia, Bing Deng, Jieping Ye, Rong Xiong, Yue Wang
    • Zhejiang University, Alibaba Cloud
    • [CVPR2025] https://arxiv.org/abs/2504.04744
    • VLM-based, 2D-3D #
  • ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning | Github
    • Zhenyang Liu, Yikai Wang, Sixiao Zheng, Tongying Pan, Longfei Liang, Yanwei Fu, Xiangyang Xue
    • Fudan University, Nanyang Technological University, Shanghai Innovation Institute, NeuHelium Co., Ltd
    • [CVPR2025] https://arxiv.org/abs/2503.23297
    • LVLM-based, 2D-3D #
  • DenseGrounding: Improving Dense Language-Vision Semantics for Ego-Centric 3D Visual Grounding |
    • Henry Zheng, Hao Shi, Qihang Peng, Yong Xien Chng, Rui Huang, Yepeng Weng, Zhongchao Shi, Gao Huang
    • Tsinghua University
    • [ICLR2025] https://arxiv.org/abs/2505.04965
    • LLM-based, 2D-3D #

Outdoor-Scenes

  • Language Prompt for Autonomous Driving | Github
    • Dongming Wu, Wencheng Han, Tiancai Wang, Yingfei Liu, Xiangyu Zhang, Jianbing Shen
    • Beijing Institute of Technology, University of Macau, MEGVII Technology, Beijing Academy of Artificial Intelligence
    • [Arxiv2023] https://arxiv.org/abs/2309.04379
    • Outdoor scene, autonomous driving
  • Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension | Github
    • Runwei Guan, Ruixiao Zhang, Ningwei Ouyang, Jianan Liu, Ka Lok Man, Xiaohao Cai, Ming Xu, Jeremy Smith, Eng Gee Lim, Yutao Yue, Hui Xiong
    • JITRI, University of Liverpool, University of Southampton, Vitalent Consulting, Xi’an Jiaotong-Liverpool University, HKUST (GZ)
    • [Arxiv2024] https://arxiv.org/abs/2405.12821
    • Outdoor scene, autonomous driving
  • Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding |
    • Yuhang Liu, Boyi Sun, Guixu Zheng, Yishuo Wang, Jing Wang, Fei-Yue Wang
    • Chinese Academy of Sciences, South China Agricultural University, Beijing Institute of Technology
    • [Arxiv2024] https://arxiv.org/abs/2405.15274
    • Outdoor scene, autonomous driving
  • LidaRefer: Outdoor 3D Visual Grounding for Autonomous Driving with Transformers |
    • Yeong-Seung Baek, Heung-Seon Oh
    • Korea University of Technology and Education
    • [Arxiv2024] https://arxiv.org/abs/2411.04351
    • Outdoor scene, autonomous driving #
  • NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving |
    • Fuhao Li, Huan Jin, Bin Gao, Liaoyuan Fan, Lihui Jiang, Long Zeng
    • Tsinghua University
    • [Arxiv2025] https://arxiv.org/abs/2503.22436
    • Outdoor scene, autonomous driving #

← Metadata

165
Stars
5
Forks
Watchers

Owner

verified publisher icon liudaizong

Metadata

😎 up-to-date & curated list of awesome 3D Visual Grounding papers, methods & resources.

Back