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/binhdv92/darknet_kriyeng
https://github.com/bobby20180331/darknet_pycharm
https://github.com/bolianchen/nuscenes_depth
https://github.com/bostondiditeam/MV3D
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/bowensong0312/darknet
https://github.com/bradenhurl/DeepGTAV-PreSIL
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/GTAVisionExport-DepthExtractor
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/PreSIL-tools
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/brbzjl/pointnet2
https://github.com/brytsknguyen/M-LOAM
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
https://github.com/bt77/pointnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/buptdbj/darknet-windows-linux
https://github.com/caihaocong/Yolov3_AlexeyAB_Version
https://github.com/caoquan95/6D-pose-project
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/cardwing/codes-for-pvkd
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
https://github.com/carterprice2/Deep_Learning_project
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/cersar/3D_detection
https://github.com/cgebbe/kaggle_pku-autonomous-driving
https://github.com/chanlilong/4D_NET_pytorch
https://github.com/charlesq34/pointnet
https://github.com/charlesq34/pointnet2
https://github.com/chenfengxu714/image2point
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/chetanmreddy/voxelnet_chetan
https://github.com/chihyanghsu0805/object_detection_yolo
https://github.com/chonepieceyb/reading-frustum-pointnets-code
https://github.com/chowkamlee81/CentrePointNet
https://github.com/cjvargasc/YOLOv2
https://github.com/cloudnine148/PHC_2nd_SPC
https://github.com/coconutzs/PointNet_zs
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/code-implementation1/Code5/tree/main/ms_rcnn
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/code-implementation1/Code7/tree/main/rcnn
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/code-implementation1/Code9/tree/main/PointNet2
https://github.com/coperception/coperception
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
https://github.com/cuifc1997/nuscene-study
https://github.com/cure-lab/magicdrive
MagicDrive: Street View Generation with Diverse 3D Geometry Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.