3D Shape Modeling and Analysis

Neueste Paper

• tensorflow-riemopt: A Library for Optimization on Riemannian Manifolds

  Oleg Smirnov · 25. November 2025

  This paper presents tensorflow-riemopt, a Python library for geometric machine learning in TensorFlow. The library provides efficient implementations of neural network layers with manifold-constrained parameters, geometric operations on Riemannian manifolds, and stochastic optimization algorithms fo…

• Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion

  Keyang Lu, Sifan Zhou, Hongbin Xu, Gang Xu, Zhifei Yang, Yikai Wang, Zhen Xiao, Jieyi Long, Ming Li · 25. November 2025

  Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we p…

• MamTiff-CAD: Multi-Scale Latent Diffusion with Mamba+ for Complex Parametric Sequence

  Liyuan Deng, Yunpeng Bai, Yongkang Dai, Xiaoshui Huang, Hongping Gan, Dongshuo Huang, Hao jiacheng, Yilei Shi · 25. November 2025

  Parametric Computer-Aided Design (CAD) is crucial in industrial applications, yet existing approaches often struggle to generate long sequence parametric commands due to complex CAD models' geometric and topological constraints. To address this challenge, we propose MamTiff-CAD, a novel CAD parametr…

• Scaling Implicit Fields via Hypernetwork-Driven Multiscale Coordinate Transformations

  Plein Versace · 25. November 2025

  Implicit Neural Representations (INRs) have emerged as a powerful paradigm for representing signals such as images, 3D shapes, signed distance fields, and radiance fields. While significant progress has been made in architecture design (e.g., SIREN, FFC, KAN-based INRs) and optimization strategies (…

• MuM: Multi-View Masked Image Modeling for 3D Vision

  David Nordstr\"om, Johan Edstedt, Fredrik Kahl, Georg B\"okman · 24. November 2025

  Self-supervised learning on images seeks to extract meaningful visual representations from unlabeled data. When scaled to large datasets, this paradigm has achieved state-of-the-art performance and the resulting trained models such as DINOv3 have seen widespread adoption. However, most prior efforts…

• Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing

  Suchetan G. Uppur, Hemant Kumar, Vaibhav Kumar · 24. November 2025

  Training autonomous driving and navigation systems requires large and diverse point cloud datasets that capture complex edge case scenarios from various dynamic urban settings. Acquiring such diverse scenarios from real-world point cloud data, especially for critical edge cases, is challenging, whic…

• Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generation

  Xiatao Sun, Chen Liang, Qian Wang, Daniel Rakita · 24. November 2025

  3D meshes are a critical building block for applications ranging from industrial design and gaming to simulation and robotics. Traditionally, meshes are crafted manually by artists, a process that is time-intensive and difficult to scale. To automate and accelerate this asset creation, autoregressiv…

• SAM 3D: 3Dfy Anything in Images

  SAM 3D Team, Xingyu Chen, Fu-Jen Chu, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, Aohan Lin, Jiawei Liu, Ziqi Ma, Anushka Sagar, Bowen Song, Xiaodong Wang, Jianing Yang, Bowen Zhang, Piotr Doll\'ar, Georgia Gkioxari, Matt Feiszli, Jitendra Malik · 21. November 2025

  We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images, where occlusion and scene clutter are common and visual recognition cues from context play a larger role. We achieve th…

• NTK-Guided Implicit Neural Teaching

  Chen Zhang, Wei Zuo, Bingyang Cheng, Yikun Wang, Wei-Bin Kou, Yik Chung WU, Ngai Wong · 20. November 2025

  Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, audio, and 3D reconstruction. However, fitting high-resolution signals demands optimizing over millions of coordinates, in…

• Iris: Integrating Language into Diffusion-based Monocular Depth Estimation

  Ziyao Zeng, Jingcheng Ni, Daniel Wang, Patrick Rim, Younjoon Chung, Fengyu Yang, Byung-Woo Hong, Alex Wong · 19. November 2025

  Traditional monocular depth estimation suffers from inherent ambiguity and visual nuisances. We demonstrate that language can enhance monocular depth estimation by providing an additional condition (rather than images alone) aligned with plausible 3D scenes, thereby reducing the solution space for d…

• NeuralSSD: A Neural Solver for Signed Distance Surface Reconstruction

  Zi-Chen Xi, Jiahui Huang, Hao-Xiang Chen, Francis Williams, Qun-Ce Xu, Tai-Jiang Mu, Shi-Min Hu · 19. November 2025

  We proposed a generalized method, NeuralSSD, for reconstructing a 3D implicit surface from the widely-available point cloud data. NeuralSSD is a solver-based on the neural Galerkin method, aimed at reconstructing higher-quality and accurate surfaces from input point clouds. Implicit method is prefer…

• A neural optimization framework for free-boundary diffeomorphic mapping problems and its applications

  Zhehao Xu, Lok Ming Lui · 18. November 2025

  Free-boundary diffeomorphism optimization is a core ingredient in the surface mapping problem but remains notoriously difficult because the boundary is unconstrained and local bijectivity must be preserved under large deformation. Numerical Least-Squares Quasiconformal (LSQC) theory, with its provab…

• The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent

  Yatin Dandi, Luca Pesce, Lenka Zdeborov\'a, Florent Krzakala · 17. November 2025

  Understanding the advantages of deep neural networks trained by gradient descent (GD) compared to shallow models remains an open theoretical challenge. In this paper, we introduce a class of target functions (single and multi-index Gaussian hierarchical targets) that incorporate a hierarchy of laten…

• Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties

  Mariafrancesca Patalano, Giovanna Capizzi, Kamran Paynabar · 11. November 2025

  Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD are widely applied in advanced manufacturing processes, including additive, subtractive, and hybrid manufacturing. To e…

• How Many Tokens Do 3D Point Cloud Transformer Architectures Really Need?

  Tuan Anh Tran, Duy M. H. Nguyen, Hoai-Chau Tran, Michael Barz, Khoa D. Doan, Roger Wattenhofer, Ngo Anh Vien, Mathias Niepert, Daniel Sonntag, Paul Swoboda · 10. November 2025

  Recent advances in 3D point cloud transformers have led to state-of-the-art results in tasks such as semantic segmentation and reconstruction. However, these models typically rely on dense token representations, incurring high computational and memory costs during training and inference. In this wor…

• Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

  Matteo Bastico, David Ryckelynck, Laurent Cort\'e, Yannick Tillier, Etienne Decenci\`ere · 10. November 2025

  As 3D point clouds become a cornerstone of modern technology, the need for sophisticated generative models and reliable evaluation metrics has grown exponentially. In this work, we first expose that some commonly used metrics for evaluating generated point clouds, particularly those based on Chamfer…

• Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning

  Philipp Dahlinger, Niklas Freymuth, Tai Hoang, Tobias W\"urth, Michael Volpp, Luise K\"arger, Gerhard Neumann · 10. November 2025

  Simulating object deformations is a critical challenge across many scientific domains, including robotics, manufacturing, and structural mechanics. Learned Graph Network Simulators (GNSs) offer a promising alternative to traditional mesh-based physics simulators. Their speed and inherent differentia…

• Simplex-FEM Networks (SiFEN): Learning A Triangulated Function Approximator

  Chaymae Yahyati, Ismail Lamaakal, Khalid El Makkaoui, Ibrahim Ouahbi, Yassine Maleh · 10. November 2025

  We introduce Simplex-FEM Networks (SiFEN), a learned piecewise-polynomial predictor that represents f: R^d -> R^k as a globally C^r finite-element field on a learned simplicial mesh in an optionally warped input space. Each query activates exactly one simplex and at most d+1 basis functions via bary…

• Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off

  Seungyong Lee, Jeong-gi Kwak · 6. November 2025

  Virtual try-on aims to synthesize a realistic image of a person wearing a target garment, but accurately modeling garment-body correspondence remains a persistent challenge, especially under pose and appearance variation. In this paper, we propose Voost - a unified and scalable framework that jointl…

• Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference

  M. H. Jalali Kanafi, S. M. S. Movahed · 6. November 2025

  In this work, we perform a simulation-based forecasting analysis to compare the constraining power of two higher-order summary statistics of the large-scale structure (LSS), the Minkowski Functionals (MFs) and the Conditional Moments of Derivative (CMD), with a particular focus on their sensitivity …

• Scheduling the Off-Diagonal Weingarten Loss of Neural SDFs for CAD Models

  Haotian Yin, Przemyslaw Musialski · 6. November 2025

  Neural signed distance functions (SDFs) have become a powerful representation for geometric reconstruction from point clouds, yet they often require both gradient- and curvature-based regularization to suppress spurious warp and preserve structural fidelity. FlatCAD introduced the Off-Diagonal Weing…

• Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problems

  Zhiwen Li, Cheuk Hin Ho, Lok Ming Lui · 5. November 2025

  Traditional methods for high-dimensional diffeomorphic mapping often struggle with the curse of dimensionality. We propose a mesh-free learning framework designed for $n$-dimensional mapping problems, seamlessly combining variational principles with quasi-conformal theory. Our approach ensures accur…

• TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding

  Quang P. M. Pham, Khoi T. N. Nguyen, Lan C. Ngo, Truong Do, Dezhen Song, Truong-Son Hy · 4. November 2025

  Scene graphs have proven to be highly effective for various scene understanding tasks due to their compact and explicit representation of relational information. However, current methods often overlook the critical importance of preserving symmetry when generating scene graphs from 3D point clouds, …

• Geometry matters: insights from Ollivier Ricci Curvature and Ricci Flow into representational alignment through Ollivier-Ricci Curvature and Ricci Flow

  Nahid Torbati, Michael Gaebler, Simon M. Hofmann, Nico Scherf · 29. Oktober 2025

  Representational similarity analysis (RSA) is widely used to analyze the alignment between humans and neural networks; however, conclusions based on this approach can be misleading without considering the underlying representational geometry. Our work introduces a framework using Ollivier Ricci Curv…

• VoMP: Predicting Volumetric Mechanical Property Fields

  Rishit Dagli, Donglai Xiang, Vismay Modi, Charles Loop, Clement Fuji Tsang, Anka He Chen, Anita Hu, Gavriel State, David I. W. Levin, Maria Shugrina · 28. Oktober 2025

  Physical simulation relies on spatially-varying mechanical properties, often laboriously hand-crafted. VoMP is a feed-forward method trained to predict Young's modulus ($E$), Poisson's ratio ($\nu$), and density ($\rho$) throughout the volume of 3D objects, in any representation that can be rendered…