# 3D Shape Modeling and Analysis

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/3d-shape-modeling-and-analysis/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the analysis, reconstruction, and representation of three-dimensional shapes using deep learning techniques, point clouds, and mesh processing. It covers topics such as 3D classification, segmentation, object recognition, shape reconstruction from single images, surface parameterization, mesh deformation, and texture mapping. |
| Domain | Physical Sciences |
| Field | Engineering |
| OpenAlex ID | t10719 |
| Works | 64 |

## Topic papers all

Showing 15 of 64.

- [ShapeNet: An Information-Rich 3D Model Repository](https://scholariq.org/papers/shapenet-an-information-rich-3d-model-repository-2/)
- [ShapeNet: An Information-Rich 3D Model Repository](https://scholariq.org/papers/shapenet-an-information-rich-3d-model-repository/)
- [Collision Detection for Deformable Objects](https://scholariq.org/papers/collision-detection-for-deformable-objects/)
- [Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era](https://scholariq.org/papers/image-based-3d-object-reconstruction-state-of-the-art-and-trends-in-the-deep/)
- [Comparison between breast volume measurement using 3D surface imaging and classical techniques](https://scholariq.org/papers/comparison-between-breast-volume-measurement-using-3d-surface-imaging-and/)
- [Anatomically based geometric modelling of the musculo-skeletal system and other organs](https://scholariq.org/papers/anatomically-based-geometric-modelling-of-the-musculo-skeletal-system-and-other/)
- [Identification of Spring Parameters for Deformable Object Simulation](https://scholariq.org/papers/identification-of-spring-parameters-for-deformable-object-simulation/)
- [A comprehensive study of three dimensional tolerance analysis methods](https://scholariq.org/papers/a-comprehensive-study-of-three-dimensional-tolerance-analysis-methods/)
- [A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION](https://scholariq.org/papers/a-benchmark-for-large-scale-heritage-point-cloud-semantic-segmentation/)
- [3D Design Using Generative Adversarial Networks and Physics-Based Validation](https://scholariq.org/papers/3d-design-using-generative-adversarial-networks-and-physics-based-validation/)
- [No-Reference Point Cloud Quality Assessment via Domain Adaptation](https://scholariq.org/papers/no-reference-point-cloud-quality-assessment-via-domain-adaptation/)
- [Three-dimensional recording of the human face with a 3D laser scanner](https://scholariq.org/papers/three-dimensional-recording-of-the-human-face-with-a-3d-laser-scanner/)
- [A novel learning-based feature recognition method using multiple sectional view representation](https://scholariq.org/papers/a-novel-learning-based-feature-recognition-method-using-multiple-sectional-view/)
- [Three-dimensional analysis of craniofacial bones using three-dimensional computer tomography](https://scholariq.org/papers/three-dimensional-analysis-of-craniofacial-bones-using-three-dimensional/)
- [Geometry and context for semantic correspondences and functionality recognition in man-made 3D shapes](https://scholariq.org/papers/geometry-and-context-for-semantic-correspondences-and-functionality-recognition/)

## Topic primary papers

Showing 15 of 17.

- [ShapeNet: An Information-Rich 3D Model Repository](https://scholariq.org/papers/shapenet-an-information-rich-3d-model-repository/)
- [Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era](https://scholariq.org/papers/image-based-3d-object-reconstruction-state-of-the-art-and-trends-in-the-deep/)
- [Identification of Spring Parameters for Deformable Object Simulation](https://scholariq.org/papers/identification-of-spring-parameters-for-deformable-object-simulation/)
- [Geometry and context for semantic correspondences and functionality recognition in man-made 3D shapes](https://scholariq.org/papers/geometry-and-context-for-semantic-correspondences-and-functionality-recognition/)
- [Simultaneous Topology and Stiffness Identification for Mass-Spring Models Based on FEM Reference Deformations](https://scholariq.org/papers/simultaneous-topology-and-stiffness-identification-for-mass-spring-models-based/)
- [Spherical Wavelet Descriptors for Content-based 3D Model Retrieval](https://scholariq.org/papers/spherical-wavelet-descriptors-for-content-based-3d-model-retrieval/)
- [Hybrid Cutting of Deformable Solids](https://scholariq.org/papers/hybrid-cutting-of-deformable-solids/)
- [Real‐time haptic manipulation and cutting of hybrid soft tissue models by extended position‐based dynamics](https://scholariq.org/papers/real-time-haptic-manipulation-and-cutting-of-hybrid-soft-tissue-models-by/)
- [FuS-GCN: Efficient B-rep based graph convolutional networks for 3D-CAD model classification and retrieval](https://scholariq.org/papers/fus-gcn-efficient-b-rep-based-graph-convolutional-networks-for-3d-cad-model/)
- [Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels](https://scholariq.org/papers/fully-convolutional-mesh-autoencoder-using-efficient-spatially-varying-kernels/)
- [Texture Mapping with Hard Constraints Using Warping Scheme](https://scholariq.org/papers/texture-mapping-with-hard-constraints-using-warping-scheme/)
- [A sparsity preserving genetic algorithm for extracting diverse functional 3D designs from deep generative neural networks](https://scholariq.org/papers/a-sparsity-preserving-genetic-algorithm-for-extracting-diverse-functional-3d/)
- [DGCNN on FPGA: Acceleration of the Point Cloud Classifier Using FPGAs](https://scholariq.org/papers/dgcnn-on-fpga-acceleration-of-the-point-cloud-classifier-using-fpgas/)
- [Improved accuracy traditional clothes pattern retrieval using VGG19 and Distance Metrics](https://scholariq.org/papers/improved-accuracy-traditional-clothes-pattern-retrieval-using-vgg19-and-distance/)
- [The 3D tooth model segmentation method based on GAC+PointMLP network](https://scholariq.org/papers/the-3d-tooth-model-segmentation-method-based-on-gac-pointmlp-network/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.