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3D Object Detection

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평가표: 67
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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/4dvlab/stcrowd

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STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

https://github.com/AlejandroBarrera/birdnet2

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BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View

https://github.com/AlmoonYsl/OPEN

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OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection

https://github.com/AutoAILab/FusionDepth

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Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

https://github.com/AutoCompSysLab/DCTNet

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A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View

https://github.com/AutoLab-SAI-SJTU/MambaFusion

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MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection

https://github.com/AutoVision-cloud/Deformable-PV-RCNN

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Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

https://github.com/AutoVision-cloud/SA-Det3D

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SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection

https://github.com/Banconxuan/RTS3D

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RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

https://github.com/Benzlxs/tDBN

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Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes

https://github.com/Bob-cheng/MDE_Attack

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Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches

https://github.com/BubblyYi/MMPedestron

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When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset

https://github.com/CVMI-Lab/ST3D

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ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

https://github.com/CarloSgaravatti/HybridLateCascadeFusion

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A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection

https://github.com/Cc-Hy/CMKD

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Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection

https://github.com/ChongjianGE/MetaBEV

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MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation

https://github.com/Coolshanlan/DDFH-active-3Ddet

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Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection

https://github.com/DerrickXuNu/OpenCOOD

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OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication

https://github.com/DerrickXuNu/v2x-vit

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V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

https://github.com/DexterJZ/eval_driving_safety

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Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

https://github.com/Div99/W-Stereo-Disp

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Wasserstein Distances for Stereo Disparity Estimation

https://github.com/Divadi/MTC_RCNN

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Multi-Modality Task Cascade for 3D Object Detection

https://github.com/Eaphan/GLENet

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GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation

https://github.com/Eaphan/Robust3DOD

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A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks

https://github.com/Fredrik00/avod

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Multimodal 3D Object Detection from Simulated Pretraining

https://github.com/Geo-Tell/AncRec

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Shape Anchor Guided Holistic Indoor Scene Understanding

https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN

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Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection

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Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

https://github.com/HaochengWan/PVT

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PVT: Point-Voxel Transformer for Point Cloud Learning

https://github.com/HuangJunJie2017/BEVDet

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BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View

https://github.com/I2RDL2/ASTAR-3D

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A*3D Dataset: Towards Autonomous Driving in Challenging Environments

https://github.com/JakubSochor/BoxCars

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BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance

https://github.com/Jamie725/RGBT-detection

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Multimodal Object Detection via Probabilistic Ensembling

https://github.com/KPeng9510/MASS

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MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding

https://github.com/KenYu910645/perspective-aware-convolution

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Perspective-aware Convolution for Monocular 3D Object Detection

https://github.com/Link2Link/FE_GCN

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S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection

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