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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/xy-guo/LIGA-Stereo

공식 코드

LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

https://github.com/yancie-yjr/dbq-ssd

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DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection

https://github.com/yangcaoai/3dgs-det

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3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection

https://github.com/yangcaoai/CoDA_NeurIPS2023

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Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection

https://github.com/yanghtr/ssgnet

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LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation

https://github.com/yanglei18/Lite-FPN

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Lite-FPN for Keypoint-based Monocular 3D Object Detection

https://github.com/yanglei18/mix-teaching

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Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection

https://github.com/yanglei18/sgv3d

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SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection

https://github.com/yangtiming/imov3d

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ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images

https://github.com/yangyangyang127/pointclip_v2

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PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning

https://github.com/yangzhaojason/DualDiff

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DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance

https://github.com/ybzh/point-dae

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Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

https://github.com/yhbcode000/Eloss-Interpretability

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Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving

https://github.com/yhbcode000/MMDetection3D-Eloss

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Eloss in the way: A Sensitive Input Quality Metrics for Intelligent Driving

https://github.com/yhc2021/avs-net

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AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding

https://github.com/yichaoshen-ms/v-detr

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V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection

https://github.com/yichen928/STRL

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Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

https://github.com/yichen928/sparsefusion

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SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection

https://github.com/yifanlu0227/coalign

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Robust Collaborative 3D Object Detection in Presence of Pose Errors

https://github.com/yikaiw/TokenFusion

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Multimodal Token Fusion for Vision Transformers

https://github.com/yinjunbo/is-fusion

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IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

https://github.com/yinjunbo/proficientteachers

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Semi-supervised 3D Object Detection with Proficient Teachers

https://github.com/yinjunbo/proposalcontrast

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ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

https://github.com/yinjunbo/ssda3d

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SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

https://github.com/yinyunie/Total3DUnderstanding

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Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image

https://github.com/ylwhxht/l4dr

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L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection

https://github.com/ylwhxht/v2x-r

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V2X-R: Cooperative LiDAR-4D Radar Fusion for 3D Object Detection with Denoising Diffusion

https://github.com/youngfly/Dynamic-Adjustment-Fusion

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Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object Detection

https://github.com/youngskkim/CRN

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CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception

https://github.com/youshaamurhij/rvcdet

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Rethinking Voxelization and Classification for 3D Object Detection

https://github.com/yungecui/link3d

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LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud

https://github.com/yurongyou/asyncdepth

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Better Monocular 3D Detectors with LiDAR from the Past

https://github.com/yurongyou/hindsight

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Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception

https://github.com/yurongyou/rote-da

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Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

https://github.com/ywangeq/PointSeg

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PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

https://github.com/ywyeli/place3d

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Is Your LiDAR Placement Optimized for 3D Scene Understanding?

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