https://github.com/ndrplz/semiparametric
공식 코드Warp and Learn: Novel Views Generation for Vehicles and Other Objects
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/nerminsamet/houghnet
공식 코드HoughNet: Integrating near and long-range evidence for visual detection
https://github.com/nexer8/se3d
공식 코드SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
https://github.com/nicholasli1995/s-3d-rcnn
공식 코드Joint stereo 3D object detection and implicit surface reconstruction
https://github.com/nicholasli1995/snvc
공식 코드https://github.com/nightmare-n/graphrcnn
공식 코드Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph
https://github.com/nomiaro/OPA
공식 코드Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
https://github.com/northsummer/lgkd
공식 코드BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
https://github.com/nullmax-vision/qaf2d
공식 코드Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors
https://github.com/nullmax-vision/simpb
공식 코드SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras
https://github.com/nutonomy/nuscenes-devkit
공식 코드https://github.com/nutonomy/second.pytorch
공식 코드PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/nv-tlabs/cosmos-drive-dreams
공식 코드Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
https://github.com/ocnflag/point2seq
공식 코드https://github.com/octree-nn/octformer
공식 코드https://github.com/open-mmlab/OpenPCDet
공식 코드PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/open-mmlab/mmdetection3d
공식 코드Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/opendrivelab/bevperception-survey-recipe
공식 코드Geometric-aware Pretraining for Vision-centric 3D Object Detection
https://github.com/opendrivelab/birds-eye-view-perception
공식 코드Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
https://github.com/oshrout/GraVoS
공식 코드https://github.com/owen-liuyuxuan/visionfactory
공식 코드Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving
https://github.com/paathelb/MEDL-U
공식 코드MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning
https://github.com/pangsu0613/CLOCs
공식 코드CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
https://github.com/pay246-git468/radarnext
공식 코드RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radar
https://github.com/peiyunh/wysiwyg
공식 코드What You See is What You Get: Exploiting Visibility for 3D Object Detection
https://github.com/phi-wol/hydra
공식 코드Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception
https://github.com/phyllish/dvdet
공식 코드https://github.com/pixie8888/mvsdet
공식 코드MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps
https://github.com/pjlab-adg/3dtrans
공식 코드Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
https://github.com/pjlab-adg/detzero
공식 코드DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
https://github.com/pmclSF/DeepCompress
공식 코드https://github.com/pointscoder/awesome-3d-object-detection-for-autonomous-driving
공식 코드3D Object Detection for Autonomous Driving: A Comprehensive Survey
https://github.com/poodarchu/Class-balanced-Grouping-and-Sampling-for-Point-Cloud-3D-Object-Detection
공식 코드Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
https://github.com/poodarchu/ConQueR
공식 코드https://github.com/prbonn/simp
공식 코드Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization
https://github.com/prismformore/multi-task-transformer
공식 코드Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.