zya3d

zya3d's Stars

• MrNeRF/awesome-3D-gaussian-splatting

  Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.

  Language:HTML6.9k22060421

• open-mmlab/mmdetection3d

  OpenMMLab's next-generation platform for general 3D object detection.

  Language:Python5.6k591.6k1.6k

• open-mmlab/OpenPCDet

  OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

  Language:Python4.9k681.5k1.3k

• Pointcept/GPT4Point

  [CVPR'24 Highlight] GPT4Point: A Unified Framework for Point-Language Understanding and Generation.

  Language:Python374242423

• Yzichen/FlashOCC

  Language:Python37129738

• HuaiyuanXu/3D-Occupancy-Perception

  [Information Fusion 2025] A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective

  32910326

• MCG-NJU/SparseOcc

  [ECCV 2024] Fully Sparse 3D Occupancy Prediction & RayIoU Evaluation Metric

  Language:Python31965628

• zya3d/Awesome-3D-Occupancy-Prediction

  Vision-based 3D occupancy prediction in autonomous driving: a review and outlook

  198506

• lvchuandong/Awesome-Multi-Camera-3D-Occupancy-Prediction

  Awesome papers and code about Multi-Camera 3D Occupancy Prediction, such as TPVFormer, SurroundOcc, PanoOcc, OccFormer, FB-OCC, SelfOcc, COTR, SparseOcc, GaussianFormer, GaussianOcc. In this repository, you will see the latest 3D occupancy prediction papers and code.

  1163010

• NotACracker/COTR

  [CVPR24] COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction

  Language:Python833107

• Bosszhe/EMIFF

  EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection

  Language:Python7821110

• hailanyi/CPD

  Commonsense Prototype for Outdoor Unsupervised 3D Object Detection (CVPR 2024)

  Language:Python62362

• ika-rwth-aachen/MultiCorrupt

  [IV2024] MultiCorrupt: A benchmark for robust multi-modal 3D object detection, evaluating LiDAR-Camera fusion models in autonomous driving. Includes diverse corruption types (e.g., misalignment, miscalibration, weather) and severity levels. Assess model performance under challenging conditions.

  Language:Jupyter Notebook582175

• CocoBoom/fsd-bev

  This is the implementation of the paper "FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection" (ECCV 2024)

  Language:Python29512

• lvchuandong/ML3DOP

  ML3DOP: A Multi-Camera and LiDAR Dataset for 3D Occupancy Perception

  Language:Python24200

• afterglow-nju/MoPL

  the github page for Test-Time Adaptation in 3D Object Detection Using Momentum-Based Pseudo-Labeling

  220