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/holytrick/AlexeyAB_darknet
https://github.com/hongfz16/DS-Net
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
https://github.com/hongru-seu/PointPilliar
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hou-yz/multiviewx
https://github.com/houseleo/pointnet
https://github.com/hova88/Lidardet
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hova88/PointPillars_MultiHead_40FPS
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hthierno/pytorch-stardist
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
https://github.com/hweersot/darknet-custom
https://github.com/hy-signal/VoxelNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/hz-ants/DenseFusion
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/ikh-innovation/roboweldar-votenet
https://github.com/irmvlab/point-mamba
https://github.com/iskandari/darknet
https://github.com/isl-org/Open3D-ML
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/j96w/DenseFusion
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/jahongir7174/EfficientNetV2
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/jahongir7174/MaskRCNN
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/jahongir7174/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/jediofgever/PointNet_Custom_Object_Detection
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/jhkim-spa/FVNet_v2
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/jhultman/PV-RCNN
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/jianing-sun/Mask-YOLO
https://github.com/jinlong17/advgps
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
https://github.com/jkboyce/hawkeye
https://github.com/jkvt2/hierloss
https://github.com/jmpap/yolov2-tensorflow-2.0
https://github.com/joheras/darknet-colab
https://github.com/johnwlambert/argoverse_cbgs_kf_tracker
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
https://github.com/jskim808/js_pointrcnn
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/k5iogura/darknetAB
https://github.com/kamwoh/Car-Model-Classification
https://github.com/karan96/NewOne
https://github.com/kargarisaac/3d-MNIST-classification-PointNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/kargarisaac/PointNet-SemSeg-VKITTI3D
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.