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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving
PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector
CornerPoint3D: Look at the Nearest Corner Instead of the Center
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
Learning Class Prototypes for Unified Sparse Supervised 3D Object Detection
SaViD: Spectravista Aesthetic Vision Integration for Robust and Discerning 3D Object Detection in Challenging Environments
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios
Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception
Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving
Which2comm: An Efficient Collaborative Perception Framework for 3D Object Detection
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection
PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds
State Space Model Meets Transformer: A New Paradigm for 3D Object Detection
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models
Point Cloud Based Scene Segmentation: A Survey
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection
RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation
Semantic-Supervised Spatial-Temporal Fusion for LiDAR-based 3D Object Detection
HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer
Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection
Evaluating the Impact of Synthetic Data on Object Detection Tasks in Autonomous Driving
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation
SparseVoxFormer: Sparse Voxel-based Transformer for Multi-modal 3D Object Detection
Accelerate 3D Object Detection Models via Zero-Shot Attention Key Pruning
Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations
A Light Perspective for 3D Object Detection
Hierarchical Cross-Modal Alignment for Open-Vocabulary 3D Object Detection
OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection
SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning
Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance
Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?
FASTer: Focal Token Acquiring-and-Scaling Transformer for Long-term 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.