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3D Object Recognition

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.

(Image credit: Look Further to Recognize Better)

Papers

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Showing 1–50 of 97 papers

TitleStatusHype
Lifting Multi-View Detection and Tracking to the Bird's Eye ViewCode2
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
BlenderProcCode2
SceneGraphNet: Neural Message Passing for 3D Indoor Scene AugmentationCode2
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
MeshWalker: Deep Mesh Understanding by Random WalksCode1
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point CloudsCode0
Aligning Text, Images, and 3D Structure Token-by-Token—0
Topology-Guided Knowledge Distillation for Efficient Point Cloud ProcessingCode0
LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition—0
Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition—0
Targeted View-Invariant Adversarial Perturbations for 3D Object RecognitionCode0
Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled DataCode0
Formula-Supervised Visual-Geometric Pre-training—0
Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
Deep Models for Multi-View 3D Object Recognition: A Review—0
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal PerturbationCode0
SUGAR: Pre-training 3D Visual Representations for Robotics—0
Fine-grained 3D object recognition: an approach and experiments—0
Deep Graph Reprogramming—0
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation—0
R2-MLP: Round-Roll MLP for Multi-View 3D Object RecognitionCode0
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition—0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
TANDEM3D: Active Tactile Exploration for 3D Object Recognition—0
Deep Optical Coding Design in Computational Imaging—0
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering—0
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation—0
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition—0
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans—0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
Sparse Depth Completion with Semantic Mesh Deformation Optimization—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
MVT: Multi-view Vision Transformer for 3D Object RecognitionCode0
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects—0
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization NetworksCode0
Spectral Processing and Optimization of Static and Dynamic 3D Geometries—0
Offboard 3D Object Detection from Point Cloud Sequences—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
Active Perception using Light Curtains for Autonomous Driving—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
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