synsin0

apollo

An open autonomous driving platform

BEVDet

Official code base for BEVDet.

BEVFormer

This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

CenterPoint-Fusion

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.

DAIR-V2X

FusionPainting

Once_MMDet3D_Playground

MMDet3D support for Once Dataset, Still in progress.

OpenLane

Large-scale Realistic 3D Lane Dataset

SRCN3D

Official implementation of SRCN3D: Sparse R-CNN 3D Surround-View Cameras 3D Object Detection and Tracking for Autonomous Driving

StreamingFlow

StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation

synsin0/SRCN3D

Official implementation of SRCN3D: Sparse R-CNN 3D Surround-View Cameras 3D Object Detection and Tracking for Autonomous Driving

synsin0/StreamingFlow

StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation

synsin0/Once_MMDet3D_Playground

MMDet3D support for Once Dataset, Still in progress.

synsin0/OpenLane

Large-scale Realistic 3D Lane Dataset

synsin0/apollo

An open autonomous driving platform

synsin0/BEVDet

Official code base for BEVDet.

synsin0/BEVFormer

This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

synsin0/CenterPoint-Fusion

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.

synsin0/DAIR-V2X

synsin0/FusionPainting

synsin0/ST3D

(CVPR 2021) ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection