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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/baurst/liso

공식 코드

LISO: Lidar-only Self-Supervised 3D Object Detection

https://github.com/benjaminrwilson/range-view-3d-detection

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What Matters in Range View 3D Object Detection

https://github.com/bjork-crypto/visled-querying

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Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

https://github.com/blakechen97/sasa

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SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection

https://github.com/blvlab/pimae

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PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

https://github.com/bob-cheng/cl-fusionattack

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Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection

https://github.com/bosszhe/emiff

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EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection

https://github.com/bosszhe/vimi

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VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection

https://github.com/bravegroup/dcd

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Densely Constrained Depth Estimator for Monocular 3D Object Detection

https://github.com/bravegroup/fullysparsefusion

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Fully Sparse Fusion for 3D Object Detection

https://github.com/bravegroup/pointsam-for-mixsup

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MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection

https://github.com/caiqi/cascasde-3d

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3D Cascade RCNN: High Quality Object Detection in Point Clouds

https://github.com/cea-list/alpi

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ALPI: Auto-Labeller with Proxy Injection for 3D Object Detection using 2D Labels Only

https://github.com/cfzd/monoground

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MonoGround: Detecting Monocular 3D Objects from the Ground

https://github.com/ch-sa/labelCloud

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labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds

https://github.com/charlesq34/frustum-pointnets

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Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/chaytonmin/occ-bev

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UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving

https://github.com/chaytonmin/occupancy-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/chaytonmin/uniscene

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UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving

https://github.com/chaytonmin/voxel-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/cheng052/BRNet

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Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

https://github.com/chengzhag/Implicit3DUnderstanding

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Holistic 3D Scene Understanding from a Single Image with Implicit Representation

https://github.com/chenhongyiyang/widthformer

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WidthFormer: Toward Efficient Transformer-based BEV View Transformation

https://github.com/chenyilun95/DSGN

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DSGN: Deep Stereo Geometry Network for 3D Object Detection

https://github.com/chenyilun95/dsgn2

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DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors

https://github.com/chreisinger/ViLGOD

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Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection

https://github.com/cliu2/mtrans

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Multimodal Transformer for Automatic 3D Annotation and Object Detection

https://github.com/cmpute/mmdetection3d

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Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss

https://github.com/cocoboom/fsd-bev

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FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection

https://github.com/coperception/robosac

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Among Us: Adversarially Robust Collaborative Perception by Consensus

https://github.com/cskkxjk/mononerd

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MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection

https://github.com/ctu-vras/pcl-augmentation

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Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation

https://github.com/ctu-vras/t-concord3d

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Teachers in concordance for pseudo-labeling of 3D sequential data

https://github.com/cvlab-epfl/mvflow

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Multi-view Tracking Using Weakly Supervised Human Motion Prediction

https://github.com/cvlab-yonsei/HVPR

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HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection

https://github.com/cvmi-lab/sparsekd

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Towards Efficient 3D Object Detection with Knowledge Distillation

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