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ZheJiang University
- HangZhou
Stars
World Model based Autonomous Driving Platform in CARLA 🚗
An open source implementation of CLIP.
Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:
[NeurIPS 2024 Datasets and Benchmarks Track] Closed-Loop E2E-AD Benchmark Enhanced by World Model RL Expert
BEVFormer, UniAD, VAD in Closed-Loop CARLA Evaluation with World Model RL Expert Think2Drive
[CVPR 2025] UniScene: Unified Occupancy-centric Driving Scene Generation
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
[CVPR 2024] LMDrive: Closed-Loop End-to-End Driving with Large Language Models
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Code for paper "MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping", ECCV 2024 (Oral)
Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline
Fast and memory-efficient exact attention
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
[ICLR24] Official implementation of the paper “MagicDrive: Street View Generation with Diverse 3D Geometry Control”
[CVPR 2023] Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark
[ICCV 2023] OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
(ECCV2024)ADMap: Anti-disturbance framework for reconstructing online vectorized HD map
[ECCV'24] Online Vectorized HD Map Construction using Geometry
[ICLR'23 Spotlight & ECCV'24 & IJCV'24] MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
XuyangBai / TransFusion
Forked from open-mmlab/mmdetection3d[PyTorch] Official implementation of CVPR2022 paper "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers". https://arxiv.org/abs/2203.11496