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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/public-bots/gaussianpretrain

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GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous Driving

https://github.com/public-bots/tigdistill-bev

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TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation

https://github.com/pufanqi23/monodgp

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MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors

https://github.com/purduedigitaltwin/redformer

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Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar Fusion

https://github.com/pxiangwu/MotionNet

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MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

https://github.com/pyun-ram/FL3D

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Focal Loss in 3D Object Detection

https://github.com/qcraftai/cross-modal-ssl

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Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds

https://github.com/qcraftai/distill-bev

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DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation

https://github.com/qcraftai/pillarnext

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PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

https://github.com/qq456cvb/CanonicalVoting

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Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes

https://github.com/quan-dao/apro3d-net

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Attention-based Proposals Refinement for 3D Object Detection

https://github.com/quan-dao/pc-corrector

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Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow

https://github.com/quan-dao/practical-collab-perception

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Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection

https://github.com/quotation2520/pg-rcnn

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PG-RCNN: Semantic Surface Point Generation for 3D Object Detection

https://github.com/radetzkyli/multi-v2x

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Multi-V2X: A Large Scale Multi-modal Multi-penetration-rate Dataset for Cooperative Perception

https://github.com/raileye3d/raileye3d_dataset

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RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety

https://github.com/rango-zhang-hang/ntu-visual-recognition-via-vision-language-model-transfer

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M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection

https://github.com/rasd3/3D-Dual-Fusion

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3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection

https://github.com/rasd3/toda

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Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection

https://github.com/rayguan97/M3DeTR

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M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

https://github.com/revisitq/Pseudo-Stereo-3D

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Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

https://github.com/richardmarcus/synth-it-like-kitti

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Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios

https://github.com/robertwyq/frustum

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FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection

https://github.com/rst-tu-dortmund/lerojd

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LEROjD: Lidar Extended Radar-Only Object Detection

https://github.com/rubbish001/co-fix3d

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Co-Fix3D: Enhancing 3D Object Detection with Collaborative Refinement

https://github.com/rui-qian/SoTA-3D-Object-Detection

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3D Object Detection for Autonomous Driving: A Survey

https://github.com/rui-qian/badet

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BADet: Boundary-Aware 3D Object Detection from Point Clouds

https://github.com/ruiyang-061x/lise

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Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene

https://github.com/ruiyum/stone

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STONE: A Submodular Optimization Framework for Active 3D Object Detection

https://github.com/runpeidong/pointdistiller

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PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

https://github.com/safetylab24/FusionCVCP

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CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction

https://github.com/sakharok13/x-ray-teacher-patching-tools

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Weak-to-Strong 3D Object Detection with X-Ray Distillation

https://github.com/saltoricristiano/SF-UDA-3DV

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SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection

https://github.com/sanjay-810/aydiv2

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AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer

https://github.com/sanjay-810/qicvt

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Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs

https://github.com/sanjay-810/savid

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SaViD: Spectravista Aesthetic Vision Integration for Robust and Discerning 3D Object Detection in Challenging Environments

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.