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3D Object Detection

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하위 태스크: 5
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/yzichen/polarbevdet

공식 코드

PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View

https://github.com/zaiweizhang/H3DNet

공식 코드

H3DNet: 3D Object Detection Using Hybrid Geometric Primitives

https://github.com/zakharos/DPOD

공식 코드

DPOD: 6D Pose Object Detector and Refiner

https://github.com/zehuichen123/autoalignv2

공식 코드

AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection

https://github.com/zehuichen123/bevdistill

공식 코드

BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection

https://github.com/zeliu98/Group-Free-3D

공식 코드

Group-Free 3D Object Detection via Transformers

https://github.com/zhanggang001/hednet

공식 코드

HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

https://github.com/zhangtravis/hist-da

공식 코드

Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features

https://github.com/zhangyp15/MonoFlex

공식 코드

Objects are Different: Flexible Monocular 3D Object Detection

https://github.com/zhangyp15/beverse

공식 코드

BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

https://github.com/zhangyp15/simmod

공식 코드

A Simple Baseline for Multi-Camera 3D Object Detection

https://github.com/zhanyang-nwpu/mono3dvg

공식 코드

Mono3DVG: 3D Visual Grounding in Monocular Images

https://github.com/zhenyuw16/uni3detr

공식 코드

Uni3DETR: Unified 3D Detection Transformer

https://github.com/zhh6425/LocalContextPropagation

공식 코드

LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers

https://github.com/zhimin-c/bridge3d

공식 코드

Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models

https://github.com/zhimin-c/multiview-mae

공식 코드

Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder

https://github.com/zhiqi-li/BEVFormer

공식 코드

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

https://github.com/zhixinwang/frustum-convnet

공식 코드

Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection

https://github.com/zhouqiu/sogdet

공식 코드

SOGDet: Semantic-Occupancy Guided Multi-view 3D Object Detection

https://github.com/zhouzheyuan/sgm3d

공식 코드

SGM3D: Stereo Guided Monocular 3D Object Detection

https://github.com/zhsun0357/Resonant-Scanned-LiDAR

공식 코드

Resonant Scanning Design and Control for Fast Spatial Sampling

https://github.com/zhuoxiao-chen/mos

공식 코드

MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection

https://github.com/zhuoxiao-chen/redb-da-3ddet

공식 코드

Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling

https://github.com/zhuxing0/sa-radar

공식 코드

Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding

https://github.com/zichengduan/mvchm

공식 코드

Multiview Detection with Cardboard Human Modeling

https://github.com/ziongo6/fadet

공식 코드

FADet: A Multi-sensor 3D Object Detection Network based on Local Featured Attention

https://github.com/zju3dv/disprcnn

공식 코드

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

https://github.com/zjulearning/srdet

공식 코드

Suppress-and-Refine Framework for End-to-End 3D Object Detection

https://github.com/zlichen/vectorformer

공식 코드

Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection

https://github.com/zllxot/core

공식 코드

CORE: Cooperative Reconstruction for Multi-Agent Perception

https://github.com/zongdai/autoshape

공식 코드

AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

https://github.com/zqyq/Mahalanobis-Distance-based-Multi-view-Optimal-Transport-for-Multi-view-Crowd-Localization

공식 코드

Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization

https://github.com/zqyq/Multi-view-People-Detection-in-Large-Scenes-via-View-wise-Contribution-Weighting

공식 코드

Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting

https://github.com/zrrskywalker/monodetr

공식 코드

MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection

https://github.com/zrrskywalker/point-m2ae

공식 코드

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

https://github.com/zubair-irshad/NeRF-MAE

공식 코드

NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields

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