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/Launch-on-Titania/DynOPETs
https://github.com/LebronGG/PointNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/LinZhuoChen/pointnet2_multi_gpu
https://github.com/Lmath11/darknet
https://github.com/Lw510107/PointNet
https://github.com/Lw510107/pointnet-2018.6.27-
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/MS-Mind/MS-Code-06/tree/main/pointpillars
https://github.com/MariefW/6.-trainingYOLO
https://github.com/Maskify/darknet
https://github.com/MaximKuklin/3D_Object_Detection_Diploma
https://github.com/Microsoft/singleshotpose
https://github.com/Mind23-2/MindCode-109
https://github.com/Mind23-2/MindCode-15
https://github.com/Mind23-2/MindCode-28
https://github.com/Mind23-2/MindCode-29
https://github.com/MindCode-4/code-5/tree/main/yolos
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
https://github.com/MindCode-4/code-8/tree/main/polyloss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-paper-code-2/code2/tree/main/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/MindSpore-paper-code-2/code3/tree/main/TokenFusion
https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
https://github.com/MindSpore-paper-code-3/code10/tree/main/frustum-pointnet
https://github.com/MindSpore-paper-code-3/code6/tree/main/CascadeRCNN
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/MindSpore-paper-code-3/code7/tree/main/frustum-pointnet
https://github.com/MindSpore-scientific-2/code-10/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific-2/code-11/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific-2/code-3/tree/main/Sub2Full-OCT-Denoising
https://github.com/MindSpore-scientific-2/code-4/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-5/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-8/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-8/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific/code-1/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific/code-10/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific/code-11/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific/code-13/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific/code-5/tree/main/polyloss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.