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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 26–50 of 97 papers

TitleStatusHype
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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