{"total":31,"rows":[{"id":"2106.09431","title":"NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go","created":"2021-06-17","cited_by_count":81,"area":null},{"id":"2106.02285","title":"Subdivision-Based Mesh Convolution Networks","created":"2021-06-04","cited_by_count":60,"area":null},{"id":"2203.07858","title":"A Survey of Non-Rigid 3D Registration","created":"2022-03-11","cited_by_count":59,"area":null},{"id":"2110.09994","title":"DPFM: Deep Partial Functional Maps","created":"2021-10-19","cited_by_count":58,"area":null},{"id":"2204.13453","title":"Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching","created":"2022-04-28","cited_by_count":36,"area":null},{"id":"2210.06373","title":"Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching","created":"2022-10-12","cited_by_count":35,"area":null},{"id":"2408.03178","title":"An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion","created":"2024-08-06","cited_by_count":34,"area":null},{"id":"2303.16527","title":"Understanding and Improving Features Learned in Deep Functional Maps","created":"2023-03-29","cited_by_count":20,"area":null},{"id":"2301.05839","title":"NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching","created":"2023-01-14","cited_by_count":18,"area":null},{"id":"2112.09546","title":"Complex Functional Maps : a Conformal Link Between Tangent Bundles","created":"2021-12-17","cited_by_count":17,"area":null},{"id":"2104.03916","title":"Field Convolutions for Surface CNNs","created":"2021-04-08","cited_by_count":14,"area":null},{"id":"2203.07694","title":"Implicit field supervision for robust non-rigid shape matching","created":"2022-03-15","cited_by_count":13,"area":null},{"id":"2111.08799","title":"DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds","created":"2021-11-16","cited_by_count":10,"area":null},{"id":"2211.06009","title":"What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives","created":"2022-11-11","cited_by_count":9,"area":null},{"id":"2204.08824","title":"Semi-supervised 3D shape segmentation with multilevel consistency and part substitution","created":"2022-04-19","cited_by_count":8,"area":null},{"id":"2212.02910","title":"G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors","created":"2022-12-06","cited_by_count":8,"area":null},{"id":"2403.06804","title":"Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction","created":"2024-03-11","cited_by_count":7,"area":null},{"id":"2303.15104","title":"Generalizable Local Feature Pre-training for Deformable Shape Analysis","created":"2023-03-27","cited_by_count":6,"area":null},{"id":"2510.14146","title":"PoissonNet: A Local-Global Approach for Learning on Surfaces","created":"2025-10-15","cited_by_count":6,"area":null},{"id":"2104.11571","title":"AttWalk: Attentive Cross-Walks for Deep Mesh Analysis","created":"2021-04-23","cited_by_count":5,"area":null},{"id":"2106.15778","title":"Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification","created":"2021-06-30","cited_by_count":4,"area":null},{"id":"2409.07327","title":"Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning","created":"2024-09-11","cited_by_count":2,"area":null},{"id":"2603.15780","title":"Parallelised Differentiable Straightest Geodesics for 3D Meshes","created":"2026-03-16","cited_by_count":2,"area":null},{"id":"2110.02279","title":"Turing approximations, toric isometric embeddings & manifold convolutions","created":"2021-10-05","cited_by_count":1,"area":null},{"id":"2111.00328","title":"Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow","created":"2021-10-30","cited_by_count":1,"area":null},{"id":"2505.18772","title":"CageNet: A Meta-Framework for Learning on Wild Meshes","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2105.13277","title":"MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation","created":"2021-05-27","cited_by_count":0,"area":null},{"id":"2108.06695","title":"U-mesh: Human Correspondence Matching with Mesh Convolutional Networks","created":"2021-08-15","cited_by_count":0,"area":null},{"id":"2306.05246","title":"A Task-driven Network for Mesh Classification and Semantic Part Segmentation","created":"2023-06-08","cited_by_count":0,"area":null},{"id":"2606.04871","title":"Recent Advances and Trends in Learning-based 3D Representations","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2607.24954","title":"Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes","created":"2026-07-27","cited_by_count":0,"area":null}],"next_cursor":null}