mlsanityData to 7 Oct 2026

A survey on Deep Learning Advances on Different 3D Data Representations

arXiv 1808.01462 ↗First posted 4 Aug 2018cs.CVcited by 25

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Abstract (arXiv)

3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes. Recently, with the availability of both large 3D datasets and computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition and correspondence. Depending on the considered 3D data representation, different challenges may be foreseen in using existent deep learning architectures. In this work, we provide a comprehensive overview about various 3D data representations highlighting the difference between Euclidean and non-Euclidean ones. We also discuss how Deep Learning methods are applied on each representation, analyzing the challenges to overcome.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2018.

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Citing papers per year: 2021: 9; 2022: 7; 2023: 1; 2024: 2; 2025: 5; 2026: 1.

25 papers.

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  1. 2021Geometric Deep Learning on Molecular Representationscited by 113
  2. 2021On Self-Contact and Human Posecited by 88
  3. 20213D Semantic Scene Completion: a Surveycited by 74
  4. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  5. 2023Generative AI meets 3D: A Survey on Text-to-3D in AIGC Eracited by 61
  6. 2022CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representationscited by 30
  7. 2025A Survey on Human Interaction Motion Generationcited by 25
  8. 20223D Vision with Transformers: A Surveycited by 21
  9. 2021Medical Image Segmentation with 3D Convolutional Neural Networks: A Surveycited by 16
  10. 2022Volumetric Disentanglement for 3D Scene Manipulationcited by 13
  11. 2021A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundationscited by 11
  12. 2024PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forestscited by 9
13 more
  1. 2021An Effective Loss Function for Generating 3D Models from Single 2D Image without Renderingcited by 7
  2. 2022ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapescited by 6
  3. 2022Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stresscited by 5
  4. 2021Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classificationcited by 4
  5. 2022Gaussian Mixture Convolution Networkscited by 4
  6. 2021UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Repscited by 3
  7. 2025MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Cloudscited by 3
  8. 2021Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directionscited by 2
  9. 2025Conditional Graph Neural Network for Predicting Soft Tissue Deformation and Forcescited by 1
  10. 2022SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathologycited by 0
  11. 2025Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshescited by 0
  12. 2025Large Language Models and 3D Vision for Intelligent Robotic Perception and Autonomycited by 0
  13. 2026Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unetcited by 0