Papers with Code 아카이브

3D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/ganwanshui/simpleoccupancy

공식 코드

A Simple Framework for 3D Occupancy Estimation in Autonomous Driving

https://github.com/garfield-cpp/hgsfusion

공식 코드

HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection

https://github.com/garrickbrazil/M3D-RPN

공식 코드

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/geonhobang/RadarDistill

공식 코드

RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features

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

공식 코드

Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds

https://github.com/ghostish/objectcentricocccompletion

공식 코드

Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection

https://github.com/giddyyupp/coco-minitrain

공식 코드

HoughNet: Integrating near and long-range evidence for visual detection

https://github.com/goodarzmehr/simbev

공식 코드

SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset

https://github.com/google-research-datasets/Objectron

공식 코드

Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

https://github.com/gorilla-lab-scut/vista

공식 코드

VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention

https://github.com/gtzly/FADNet

공식 코드

Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation

https://github.com/gujiaqivadin/HomographyLoss

공식 코드

Homography Loss for Monocular 3D Object Detection

https://github.com/gwenzhang/GGA

공식 코드

General Geometry-aware Weakly Supervised 3D Object Detection

https://github.com/gwenzhang/voxel-mamba

공식 코드

Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection

https://github.com/gwxuan/tsp3d

공식 코드

Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding

https://github.com/gyhandy/3d-copy-paste

공식 코드

3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection

https://github.com/haibao-yu/ffnet-vic3d

공식 코드

Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction

https://github.com/hailanyi/cpd

공식 코드

Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

https://github.com/hailanyi/virconv

공식 코드

Virtual Sparse Convolution for Multimodal 3D Object Detection

https://github.com/haiyang-w/cagroup3d

공식 코드

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

https://github.com/haiyang-w/dsvt

공식 코드

DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets

https://github.com/haiyang-w/rbgnet

공식 코드

RBGNet: Ray-based Grouping for 3D Object Detection

https://github.com/haiyang-w/unitr

공식 코드

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation

https://github.com/hama-dl-dev/sesame

공식 코드

SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics

https://github.com/hanchaa/meformer

공식 코드

Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble

https://github.com/hancyran/RepSurf

공식 코드

Surface Representation for Point Clouds

https://github.com/haoranzhuexplorer/ad-l-jepa-release

공식 코드

AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data

https://github.com/haoy945/demf

공식 코드

Boosting 3D Object Detection via Object-Focused Image Fusion

https://github.com/happinesslz/EPNet

공식 코드

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

https://github.com/happinesslz/LION

공식 코드

LION: Linear Group RNN for 3D Object Detection in Point Clouds

https://github.com/happinesslz/epnetv2

공식 코드

EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection

https://github.com/happinesslz/fbmnet

공식 코드

Multi-Modal 3D Object Detection by Box Matching

https://github.com/heng-hw/spacap3d

공식 코드

Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds

https://github.com/hht1996ok/ea-bev

공식 코드

EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection

https://github.com/hitsz-nrsl/de6d

공식 코드

Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness

https://github.com/hlesmqh/WS3D

공식 코드

Weakly Supervised 3D Object Detection from Lidar Point Cloud

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