𝗗𝗮𝘆-𝟭𝟴𝟵 Computer Vision Learning 𝗦𝗡𝗘-𝗥𝗼𝗮𝗱𝗦𝗲𝗴: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection by HKUST Robotics Institute Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in ECCV2020 with over 23 citations. 🔸 It outperforms FuseNet, RTFNet, DepthAware CNN, MFNet etc. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eUm5gbk Code : https://lnkd.in/ea4k9xq Dataset : https://lnkd.in/ePiDCQ9 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Freespace detection is an essential component of visual perception for self-driving cars. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. 🔸Novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Furthermore, they propose a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surface normal information for accurate freespace detection. 🔸For research purposes, we publish a large-scale synthetic freespace detection dataset, named Ready-to-Drive (R2D) road dataset, collected under different illumination and weather conditions. #computervision #artificialintelligence #data