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3D Object Recognition
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3D object recognition is the task of recognising objects from 3D data.
Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards.
<span style="color:grey; opacity: 0.6">(Image credit: Look Further to Recognize Better)</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Aligning Text, Images, and 3D Structure Token-by-Token
Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing
LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition
Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition
Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition
Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
Formula-Supervised Visual-Geometric Pre-training
Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding
Deep Models for Multi-View 3D Object Recognition: A Review
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation
SUGAR: Pre-training 3D Visual Representations for Robotics
Lifting Multi-View Detection and Tracking to the Bird's Eye View
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects
Fine-grained 3D object recognition: an approach and experiments
Deep Graph Reprogramming
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models
TANDEM3D: Active Tactile Exploration for 3D Object Recognition
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
Deep Optical Coding Design in Computational Imaging
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans
On Automatic Data Augmentation for 3D Point Cloud Classification
Sparse Depth Completion with Semantic Mesh Deformation Optimization
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
MVT: Multi-view Vision Transformer for 3D Object Recognition
Explainability-Aware One Point Attack for Point Cloud Neural Networks
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks
Spectral Processing and Optimization of Static and Dynamic 3D Geometries
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