Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟬𝟳 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Block-NeRF: Scalable Large Scene Neural View Synthesis by Waymo Follow me for a similar post: @Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: Block-NeRF: Scalable Large Scene Neural View Synthesis 🔸 This paper is published arxiv2022. 🔸What is NeRF? A: Neural Radiance Field or NeRF is a method for generating novel views of complex scenes. NeRF takes a set of input images of a scene and renders the complete scene by interpolating between the scenes. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to decompose the scene into individually trained NeRFs. 🔸This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment. 🔸We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions. 🔸We add appearance embeddings, learned pose refinement, and controllable exposure to each individual NeRF, and introduce a procedure for aligning appearance between adjacent NeRFs so that they can be seamlessly combined. 🔸We build a grid of Block-NeRFs from 2.8 million images to create the largest neural scene representation to date, capable of rendering an entire neighborhood of San Francisco. #computervision #artificialintelligence #data