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/keshav47/Face-Recognition-And-Verification
https://github.com/khaled2ahmed/k2a
https://github.com/kirilcvetkov92/Vehicle-Detection
https://github.com/kiruthick101/darknet
https://github.com/km1414/CNN-models
https://github.com/kongzhiyou/darknet-master
https://github.com/konyul/mmdet3d
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/krullgit/realsense_3d_detection
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/ksm26/spatiotemporal-predictions
https://github.com/kujason/ip_basic
Joint 3D Proposal Generation and Object Detection from View Aggregation
https://github.com/kvijaykrish/Bosch_AI_VehicleClassification
https://github.com/kyunghwan/darknet_v3
https://github.com/lcxzzz0113/CenterNet
https://github.com/lee-man/movenet
https://github.com/leobean/CenterNet_oriented
https://github.com/leofansq/CBR
Calibration-free BEV Representation for Infrastructure Perception
https://github.com/leon-liangwu/MaskYolo_Caffe
https://github.com/leon-liangwu/py-caffe-yolo
https://github.com/li-yibing/oceannet
https://github.com/lilanxiao/Rotated_IoU
https://github.com/lingzhang1/pointnet_tensorflow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/livox-sdk/livox_detection
https://github.com/lixiaoyu2000/fastpoly
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
https://github.com/llzlcl/pointcloud-segment
https://github.com/longcw/yolo2-pytorch
https://github.com/lumia-group/apl
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/lyqcom/models-master
https://github.com/lzccccc/3d-bounding-box-estimation-for-autonomous-driving
https://github.com/m-parchami/3DVehicleDetection
https://github.com/manankshastri/Object-Detection
https://github.com/manishravula/yolov3_tiny_pruned
https://github.com/mapeAAU/3D-ZeF
https://github.com/mathieuorhan/darknet
https://github.com/maudzung/CenterNet3D-PyTorch
https://github.com/maudzung/Complex-YOLOv4-Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.