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/Vijayabhaskar96/Object-Detection-Algorithms
https://github.com/WARM-3D/WARM-3D
https://github.com/WYGNG/CenterNet-Pro
https://github.com/WYGNG/USTC_SSE_Project
https://github.com/Wangxy2180/darknetKinectDetect
https://github.com/Wastoon/XinTong_CenterNet
https://github.com/WordBearerYI/VoxelNet-pytorch
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/XuLena/3D-sensing
https://github.com/XuanjieXiao/vehicle-detection-by-using-camera
https://github.com/Yang2446/pointnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Yc174/frustum-pointnets
https://github.com/YiruS/pointnet_adversarial
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Yongjjun/singleshotpose
https://github.com/Yotonctu/densefusion_torch1.0
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/ZRandomize/MatrixVT
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
https://github.com/Zengyi-Qin/TLNet
Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
https://github.com/ZhenJie-Zhang/darknet_shoe
https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/ZhixinLai/3D-detection-with-monocular-RGB-image
https://github.com/a-nau/easy-image-scraping
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
https://github.com/a-nau/image-selection-and-cnn-training
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
https://github.com/aakashjhawar/traffic-sign-detection
https://github.com/aaron-wang-de/ab
https://github.com/aaronfriedman6/MV3D_VoxelNet
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/abhi1kumar/M3D-RPN
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/abhigoku10/CenterPoint_PC
https://github.com/abhuse/polyloss-pytorch
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/adept-thu/SATGCN
https://github.com/agutuyen-dev/darknet
https://github.com/ahhan02/darknet-alex
https://github.com/ahmed-anas/thesis-pointnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/ai4ce/FLAT
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
https://github.com/aimerykong/RGBT-detection
https://github.com/aimotive/mm_training
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.