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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
BlenderProcCode2
SceneGraphNet: Neural Message Passing for 3D Indoor Scene AugmentationCode2
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
MeshWalker: Deep Mesh Understanding by Random WalksCode1
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Aligning Text, Images, and 3D Structure Token-by-Token—0
Analysis by Synthesis: 3D Object Recognition by Object Reconstruction—0
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching—0
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition—0
Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation—0
Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images—0
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Deep Graph Reprogramming—0
Deep Models for Multi-View 3D Object Recognition: A Review—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Optical Coding Design in Computational Imaging—0
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions—0
Dual coordinate solvers for large-scale structural SVMs—0
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition—0
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures—0
Fine-grained 3D object recognition: an approach and experiments—0
Formula-Supervised Visual-Geometric Pre-training—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
3D Object Recognition with Deep Belief Nets—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
Active Perception using Light Curtains for Autonomous Driving—0
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks—0
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans—0
Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition—0
Offboard 3D Object Detection from Point Cloud Sequences—0
Orientation-boosted Voxel Nets for 3D Object Recognition—0
Performance Evaluation of 3D Correspondence Grouping Algorithms—0
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering—0
Representation Learning on Unit Ball with 3D Roto-Translational Equivariance—0
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition—0
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching—0
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects—0
Sparse Depth Completion with Semantic Mesh Deformation Optimization—0
Spectral Processing and Optimization of Static and Dynamic 3D Geometries—0
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