Papers with Code 아카이브
3D Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 67
- 보존된 평가 기록
- 806
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/youssef-kishk/ROS-Based-Robot-Object-Detection-Recognition
https://github.com/yshah43/DPOD
https://github.com/ytng001/sensemaking
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/yuanxy92/darknet
https://github.com/yudie433/darknet
https://github.com/yuliani29/yolotraining
https://github.com/yungecui/bow3d
LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud
https://github.com/yytang2012/darknet
https://github.com/zenroad/modifypointnet
https://github.com/zghera/pvcnn-tf
https://github.com/zghera/voxel-tf-ops
https://github.com/zgx0534/pointnet_win
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/zhangxiawen/darknet-master
https://github.com/zhaott618/centernet_codes_notes
https://github.com/zhijie-yang/zhijie-yang.github.io
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
https://github.com/zhreshold/mxnet-yolo
https://github.com/zhuMingXu/rearHeadLightDetection
https://github.com/zhulf0804/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/zhyings/cbm
https://github.com/zion-king/Center-based-3D-Object-Detection-and-Tracking
https://github.com/zj463261929/darknet_mAP
https://github.com/zrrskywalker/i2p-mae
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
https://github.com/zrrskywalker/pointclip
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
https://github.com/zxhuang97/Official-implementation-StyleMixer
https://gitlab.com/eavise/lightnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.