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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/cxy1997/3D_adapt_auto_driving

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Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

https://github.com/cxy1997/DiffuBox

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DiffuBox: Refining 3D Object Detection with Point Diffusion

https://github.com/daniel-xsy/bev-attack

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On the Adversarial Robustness of Camera-based 3D Object Detection

https://github.com/darrenjkt/SEE-VCN

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Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

https://github.com/darrenjkt/ms3d

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MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection

https://github.com/darrenqu/sicp

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SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles

https://github.com/datongjie/bevspread

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BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection

https://github.com/deeptibhegde/attentiveprototypesfuda

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Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection

https://github.com/derrickxunu/cobevt

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CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

https://github.com/derrickxunu/mpda

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Bridging the Domain Gap for Multi-Agent Perception

https://github.com/detectRecog/ZoomNet

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ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection

https://github.com/dingmyu/D4LCN

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Learning Depth-Guided Convolutions for Monocular 3D Object Detection

https://github.com/dingmyu/doubly-robust-self-training

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Doubly Robust Self-Training

https://github.com/divadi/solofusion

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Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection

https://github.com/djamahl99/findnpropagate

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Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments

https://github.com/djiajunustc/H-23D_R-CNN

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From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection

https://github.com/djiajunustc/Voxel-R-CNN

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Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

https://github.com/djning/see_beyond_seeing

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Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation

https://github.com/dkoguciuk/ensemble_learning_for_point_clouds

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3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models

https://github.com/dongfang-steven-yang/faraway-frustum

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Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion

https://github.com/dop0/dvpe

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DVPE: Divided View Position Embedding for Multi-View 3D Object Detection

https://github.com/drilistbox/3dppe

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3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers

https://github.com/drl-casia/mvm3d

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MVM3Det: A Novel Method for Multi-view Monocular 3D Detection

https://github.com/dschinagl/occam

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OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data

https://github.com/dsshim0125/SwinDepth

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SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network

https://github.com/duyuwen-duen/roco-sim

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RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation

https://github.com/dvlab-research/VoxelNeXt

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VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

https://github.com/dvlab-research/focalsconv

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Focal Sparse Convolutional Networks for 3D Object Detection

https://github.com/dvlab-research/largekernel3d

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LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs

https://github.com/dvlab-research/sphereformer

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Spherical Transformer for LiDAR-based 3D Recognition

https://github.com/dvlab-research/uvtr

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Unifying Voxel-based Representation with Transformer for 3D Object Detection

https://github.com/dvlab-research/vff

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Voxel Field Fusion for 3D Object Detection

https://github.com/dyxm/pm-hmcw

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Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction Sites

https://github.com/dyxm/vcvw-3d

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VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations

https://github.com/dyzhang09/sam3d

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SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

https://github.com/eaphan/stemd

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Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection

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