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3D Object Detection

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평가표: 67
보존된 평가 기록: 806

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/mcg-nju/sparsebev

공식 코드

SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos

https://github.com/mediabrain-sjtu/where2comm

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Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps

https://github.com/megvii-basedetection/bevdepth

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BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection

https://github.com/megvii-basedetection/bevstereo

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BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo

https://github.com/megvii-research/cvpr2023-unidistill

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UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View

https://github.com/megvii-research/far3d

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Far3D: Expanding the Horizon for Surround-view 3D Object Detection

https://github.com/megvii-research/petr

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PETR: Position Embedding Transformation for Multi-View 3D Object Detection

https://github.com/meharkhurana03/cm3d

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Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection

https://github.com/mengtan00/geobev

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GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection

https://github.com/mengtan00/sa-bev

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SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detection

https://github.com/meteahishali/CSENDistance

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Representation Based Regression for Object Distance Estimation

https://github.com/mickeykang16/ev3dod

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Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras

https://github.com/microsoft/swin3d

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Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets

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Disentangling 3D Prototypical Networks For Few-Shot Concept Learning

https://github.com/mikasa3lili/ssd-monodetr

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SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection

https://github.com/mileyan/Pseudo_Lidar_V2

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Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

https://github.com/mileyan/pseudo-LiDAR_e2e

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End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

https://github.com/mileyan/pseudo_lidar

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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

https://github.com/milinpatel07/SOTIF-PCOD

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Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case

https://github.com/mindspore-ai/models/tree/master/research/cv/TokenFusion

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Multimodal Token Fusion for Vision Transformers

https://github.com/mit-han-lab/bevfusion

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BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

https://github.com/mit-han-lab/pvcnn

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Point-Voxel CNN for Efficient 3D Deep Learning

https://github.com/mit-han-lab/spvnas

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Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/mjseong0414/CRT-Fusion

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CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection

https://github.com/mjseong0414/RCM-Fusion

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RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection

https://github.com/mmocking/riders

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RIDERS: Radar-Infrared Depth Estimation for Robust Sensing

https://github.com/monster-ghost/monodistill

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MonoDistill: Learning Spatial Features for Monocular 3D Object Detection

https://github.com/mpitropov/cadc_devkit

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Canadian Adverse Driving Conditions Dataset

https://github.com/mrnabati/CenterFusion

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CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

https://github.com/mrnabati/RRPN

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RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles

https://github.com/mrqrs/cg-ssd

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CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud

https://github.com/mrsempress/obmo

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OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection

https://github.com/msundyy/faster

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FASTer: Focal Token Acquiring-and-Scaling Transformer for Long-term 3D Object Detection

https://github.com/mumianyuxin/M3DSSD

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M3DSSD: Monocular 3D Single Stage Object Detector

https://github.com/mystorm16/bsh-det3d

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

https://github.com/naomiex/mdha

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MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object Detection

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