4D Panoptic Lidar Segmentation
A paper list of lane detection.
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes
绝妙的个人生产力（Awesome Productivity 中文版）
The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
CVPR 2021 论文和开源项目合集
Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]
Paper reading notes on Deep Learning and Machine Learning
Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)
Visualizer for neural network, deep learning, and machine learning models
One Million Scenes for Autonomous Driving
Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)
A Python library for common tasks on 3D point clouds
A REAL-TIME 3D detection network [Pointpillars] compiled by CUDA/TensorRT/C++.
The proposed approach enhances the CenterPoint baseline with a multimodal fusion mechanism. First, inspired by PointPainting, an off-the-shelf Mask-RCNN model trained from nuImages is employed to generate 2D object mask information based on the camera images. Furthermore, the Cylinder3D is also adopted to produce the 3D semantic information of the input LiDAR point cloud. Then, an improved version of CenterPoint takes the painted points(with 2D instance segmentation and 3D semantic segmentation) as inputs for accurate object detection. Specifically, we replace the RPN module in CenterPoint with modified Spatial-Semantic Feature Aggregation(SSFA) to well address multi-class detection. A simple pseudo labeling technique is also integrated in a semi-supervised learning manner. In addition, the Test Time Augmentation(TTA) strategy including multiple flip and rotation operations is applied during the inference time. Finally, the detections generated from multiple voxel resolutions (0.05m to 0.125m) are assembled with 3D Weighted Bounding Box Fusion(WBF) technique to produce the final results.
Differentiable IoU of rotated bounding boxes using Pytorch
Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving (ICCV 2021)
You Only Look Once for Panopitic Driving Perception.（https://arxiv.org/abs/2108.11250）
🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥