Papers with Code 아카이브
3D Object Detection
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3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: AVOD )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SH-Tan/RangeRcnn-backbone
RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/ShaharrHorn/Detection-Game-logic
https://github.com/Shraddha2013/customyolo
https://github.com/Shraddha2013/darknett
https://github.com/Shraddha2013/darknetyol
https://github.com/Shraddha2013/darknetyolo
https://github.com/Shraddha2013/darknetyolo3
https://github.com/Shraddha2013/mydarknetyolo
https://github.com/Shraddha2013/myfileyolo
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/Sidu28/Voxel_att
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
https://github.com/SmallMunich/nutonomy_pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/Smiler-Jin/frustum_pointnet
https://github.com/Song-Jingyu/PointPainting
https://github.com/SoonminHwang/rgbt-ped-detection
https://github.com/Spiideo/mmpose
https://github.com/Srijan2001/Keypoint-regression
https://github.com/Ssong24/CenterNet_Custom
https://github.com/StanfordVL/MinkowskiEngine
Generative Sparse Detection Networks for 3D Single-shot Object Detection
https://github.com/Stick-To/CenterNet-tensorflow
https://github.com/Suci2609/generate_train_list.py-
https://github.com/Sujith2k/darknet
https://github.com/Sushma07/dancedarknet
https://github.com/THU17cyz/3DIoUMatch-PVRCNN
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
https://github.com/Techyee/darknet_resource
https://github.com/ThanhDinhDat/center_net_points
https://github.com/TheClub4/car-detection-yolov2
https://github.com/TheHidden1/YOLO.JS
https://github.com/Theopetitjean/DenseFusion_R_Invariant
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/TiagoCortinhal/SalsaNext
https://github.com/Traffic-X/MonoUNI
https://github.com/TuSimple/RangeDet
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
https://github.com/Uuuuuuho/Pillar_on_SoC
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/VaanHUANG/CSCI5210HW1
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.