Stars
The best OSS video generation models, created by Genmo
[ICRA 2024] Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving
Posters for all 235 cvpr2023 highlight papers
ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision (CVPR 2023)
A curated list of awesome 3d generation papers
[CVPR 2023] SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstruction
💭 Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)
[AAAI 2021] EMLight: Lighting Estimation via Spherical Distribution Approximation, [TIP] GMLight: Lighting Estimation via Geometric Distribution Approximation
AAAI2023，implementation of "READ: Large-Scale Neural Scene Rendering for Autonomous Driving", the experimental results are significantly better than Nerf-based methods
[SIGGRAPH Asia 2022] Text2Light: Zero-Shot Text-Driven HDR Panorama Generation
[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds (ECCV 2022) 🔥
Official Pytorch implementation of PANDORA: Polarization-Aided Neural Decomposition of Radiance
Queries on neural implicit surfaces via range analysis: ray casting, intersection, closest point, & more. SIGGRAPH 2022 paper. JAX implementation.
One Million Scenes for Autonomous Driving
NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).
Official respository for "Band-limited Coordinate Networks for Multiscale Scene Representation" | CVPR 2022
Mitsuba 3: A Retargetable Forward and Inverse Renderer
Source code for "Differentiable Signed Distance Function Rendering" (Siggraph 2022)
A curated list of papers, code and resources pertaining to image composition/compositing or object insertion/addition/compositing, which aims to generate realistic composite image.
Easy NeRF synthetic dataset creation within Blender
Point-NeRF: Point-based Neural Radiance Fields
Instant-ngp in pytorch+cuda trained with pytorch-lightning (high quality with high speed, with only few lines of legible code)