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mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
News: We released the codebase v0.14.0. In the recent nuScenes 3D detection challenge of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.
point-cloud pytorch object-detection 3d-object-detectionComplex-YOLOv4-Pytorch - The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
Use GIoU loss of rotated boxes for optimization.
real-time multiprocessing lidar object-detection mosaic lidar-point-cloud 3d-object-detection data-parallel-computing complex-yolo giou mish yolov4 rotated-boxes rotated-boxes-iouDet3D - A general 3D object detection codebse.
A general 3D Object Detection codebase in PyTorch. Please refer to INSTALATION.md.
point-cloud object-detection kitti 3d-object-detection nuscenesSFA3D - Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)
The instructions for setting up a virtual environment is here. Download the 3D KITTI detection dataset from here.
real-time ros kitti-dataset center lidar-point-cloud 3d-object-detection fast-detection rtm3d bevmapmonoloco - A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation
This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the video teaser of the library on YouTube.
machine-learning computer-vision deep-learning pytorch uncertainty object-detection human-pose-estimation kitti-dataset pose-estimation 3d-vision 3d-deep-learning 3d-detection 3d-object-detection iccv2019 pifpaf covid-19 openpifpaf icra20213d-bat - 3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling
1. Step: draw bounding box in the camera image 2. Step: choose current bounding box by activating it 3. Step: You can move it in image space or even change its size by drag and droping 4. Step: Switch into PCD MODE into birds-eye-view 5. Step: Place 3D label into 3D scene to corresponding 2D label 6. Step: Adjust label: 1. drag and dropping directly on label to change position or size 2. use control bar to change position and size (horizontal bar -> rough adjustment, vertical bar -> fine adjustment) 3. Go into camera view to check label with higher intensity and bigger point size 7. Step: Choose label from drop down list 8. Step: Repeat steps 1-7 for all objects in the scene 9. Step: Save labels into file 10. Step: Click on 'HOLD' button if you want to keep the same label positions and sizes 11. Step: click on 'Next camera image'
multi-platform web annotation tool interpolation detection point-cloud automatic autonomous-driving mechanical-turk 3d 2d active-learning pointcloud semi-automatic surround 3d-object-detection bounding-box multi-viewalfred - alfred-py: A deep learning utility library for visualization and sensor fusion purpose
Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then alfred is what you want. it's built for AI.
deeplearning video-combiner pytorch segmentation network 3d-object-detection sensor-fusionAwesome-Autonomous-Driving-Papers - This repository provides awesome research papers for autonomous driving perception
This repository provides awesome research papers for autonomous driving perception. I have tried my best to keep this repository up to date. 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.
real-time fusion rgb lidar sunrgbd kitti-dataset monocular lidar-point-cloud 3d-object-detection nuscenes two-stage waymo single-stage waymo-open-dataset pseudo-lidarCenterNet3D-PyTorch - Unofficial PyTorch implementation of the paper: "CenterNet3D: An Anchor free Object Detector for Autonomous Driving"
For mayavi library, please refer to the installation instructions from its official website. Download the 3D KITTI detection dataset from here.
voxel lidar object-detection voxelnet sparse-convolution 3d-object-detection centernet centernet3d spconvRTM3D - Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)
Generate heatmap for the center and vertexes of objects as the CenterNet paper. If you want to use the strategy from RTM3D paper, you can pass the dynamic-sigma argument to the train.py script. Download the 3D KITTI detection dataset from here.
real-time pytorch self-driving-car autonomous-driving autonomous-vehicles kitti-dataset 3d-object-detection pytorch-implementation monocular-images centernet rtm3dYOLO3D-YOLOv4-PyTorch - YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (ECCV 2018)
For mayavi and shapely libraries, please refer to the installation instructions from their official websites. Download the 3D KITTI detection dataset from here.
real-time point-cloud object-detection darknet distributed-training 3d-object-detection yolov4 rotated-boxes-iou yolo3dDeep3DBox - Deep3DBox's MXNet implementation. (In Progress: %95)
Deep3DBox's MXNet implementation.
computer-vision object-detection autonomous-driving kitti 3d-object-detectionViP - A New 3D Detector. Code Will be made public.
code will be made public.
computer-vision point-cloud kitti 3d-object-detection nuscenesmonstereo - MonoLoco++ and MonStereo for 3D localization, orientation, bounding box dimensions and social distancing from monocular and / or stereo images
MonoLoco++ and MonStereo for 3D localization, orientation, bounding box dimensions and social distancing from monocular and / or stereo images. PyTorch Official Implementation.
orientation autonomous-vehicles pedestrians 3d-object-detection covid-19 social-distancingbat-3d - 3D Bounding Box Annotation Tool 2
1. Step: draw bounding box in the camera image 2. Step: choose current bounding box by activating it 3. Step: You can move it in image space or even change its size by drag and droping 4. Step: Switch into PCD MODE into birds-eye-view 5. Step: Place 3D label into 3D scene to corresponding 2D label 6. Step: Adjust label: 1. drag and dropping directly on label to change position or size 2. use control bar to change position and size (horizontal bar -> rough adjustment, vertical bar -> fine adjustment) 3. Go into camera view to check label with higher intensity and bigger point size 7. Step: Choose label from drop down list 8. Step: Repeat steps 1-7 for all objects in the scene 9. Step: Save labels into file 10. Step: Click on 'HOLD' button if you want to keep the same label positions and sizes 11. Step: click on 'Next camera image'
cloud web annotation tool interpolation point-cloud point automatic lidar labeling pointcloud 3d-object-detection 3d-bounding-box 2d-object-detection
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