There are 3 repositories under waymo-open-dataset topic.
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (CVPR 2023)
A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.
3D Object Detection for Autonomous Driving: A Comprehensive Survey (IJCV 2023)
USB: Universal-Scale Object Detection Benchmark (BMVC 2022)
[CVPR 2023] Query-Centric Trajectory Prediction
Solution for Waymo Motion Prediction Challenge 2022. Our implementation of MultiPath++
LiDAR R-CNN: An Efficient and Universal 3D Object Detector
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds (CVPR 2023)
A toolkit for Waymo Open Dataset <-> KITTI conversions
A tool converting Waymo dataset format to Kitti dataset format.
This repository provides awesome research papers for autonomous driving perception. If you do find a problem or have any suggestions, please raise this as an issue or make a pull request with information (format of the repo): Research paper title, datasets, metrics, objects, source code, publisher, and year.
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection (NeurIPS 2022)
Progressive Coordinate Transforms for Monocular 3D Object Detection, NeurIPS 2021
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023.
Implementation of "MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving" for Waymo Open Motion Dataset
三维点云数据集下载sh脚本(目标检测,语义分割, ...)
Submission for Waymo Open Dataset Challenge 2020 - 3D Tracking
A very simple toolkit to extract data from Waymo Open Dataset
Some useful algorithms implemented in Python
Course submission material for Lidar point cloud based 3D Detection using Yolo, followed by Sensor Fusion and Camera Based Tracking using Extended Kalman Filters for Udacity Self Driving Nanodegree
Monocular object detector implementation for Waymo multi-view training and evaluation. Includes code for adaption, evaluation, data augmentation and configuration.
Exercises on Waymo Open Dataset Visualization, Object Detection, Extended Kalman Filter and Multi Target Tracking for Course 2 of the Udacity Self-Driving Car Engineer Nanodegree Program
Official Pytorch Implementation of MotionPerceiver: Real-Time Occuapncy Forecasting for Embedded Systems
Course submission material for Sensor Fusion and Camera based tracking using Extended Kalman Filters for Udacity Self Driving Nanodegree.
Object detection in urban environments using transfer learning with TensorFlow Object Detection API and AWS SageMaker. Trained on Waymo Open Dataset.
A tool converting Waymo open dataset to Kitti dataset format for easy integration into most object detector dataloaders. The tool handles range image to point cloud conversion, label transformation and supports all camera views of Waymo.
Projects developed during gaining the self-driving car nano-degree from Udacity
:car: Single Shot Detector for Autonomous Vehicle Vision