Stars
123D: A Unified Library for Multi-Modal Autonomous Driving Data
Official PyTorch implementation of our paper "Dispersing Prompt Expansion for Class-Agnostic Object Detection" (NeurIPS 2024)
A curated list of awesome LLM/VLM/VLA/World Model for Autonomous Driving(LLM4AD) resources (continually updated)
(CVPR 2025 highlight✨) Official repository of paper "LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models"
[NeurIPS 2024] Official code of ”LION: Linear Group RNN for 3D Object Detection in Point Clouds“
[CVPR 2025] RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds
[CVPR 2025] 3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer
[NeurIPS 2025] OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
[ECCV 2024] Official PyTorch Code of SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction
UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps
Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
Parser for GPMF™ formatted telemetry data used within GoPro® cameras.
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Exact method for visualizing partitions of a Deep Neural Network, CVPR 2023 Highlight
Deep Networks Grok All the Time and Here is Why
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)
Utility scripts to do camera calibration using OpenCV and check the results in Blender
ALSO: Automotive Lidar Self-supervision by Occupancy estimation
[NeurIPS'23 Spotlight] Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
💭 Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)
commaVQ is a dataset of compressed driving video
OpenMMLab's next-generation platform for general 3D object detection.
A DETR-style framework for open-vocabulary detection (OVD). CVPR 2023
A curated list of awesome papers on dataset distillation and related applications.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.