3D Shape Modeling and Analysis

Derniers papiers

• SR-JEPA: Learning Predictive Latent State in 3D Scenes

  Zihan Zhou, Qifu Wen, Xi Zeng · 7 août 2026

  Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce. We ask what a trained predictive pathway itself infers when an entire entity is absent from a native 3D scene.…

• WorldClaw: Agentic 3D Open-World Generation at Scale

  Chunchao Guo, Jinpeng Li, Yang Li, Zilong Huang · 7 août 2026

  Generating large-scale, freely explorable 3D worlds from open-ended text remains challenging because a system must jointly maintain global spatial coherence, rich local content, and explicit assets suitable for downstream editing and reuse. We present WorldClaw, a fully agentic, coarse-to-fine frame…

• 3DZip: Spatial-Aware Feature Diversity-Guided Token Compression for 3D Question Answering

  Changwoo Baek, Kyeongbo Kong · 4 août 2026

  Recent 3D vision-language models (3D VLMs) construct geometry aware tokens by projecting 2D visual features into world coordinates, enabling spatial reasoning for tasks such as 3D question answering. However, this design generates thousands of tokens per scene, resulting in substantial computational…

• PartMat: Material-Aware 3D Part Decomposition with a Single Global Latent

  Guangming Fu, Jin Song, Yiyun Fei, Guoqiu Li, Ruigao Yang, Jianan Jiang · 4 août 2026

  Part-level 3D generation has recently attracted increasing attention for producing structured and editable 3D assets. However, existing methods typically decompose objects according to functional semantics rather than the editable material boundaries (e.g., fabric, wood, metal) required in practical…

• The Push-Forward Transform for Continuous and Robust Comparison of Dynamic Shapes

  Roua Rouatbi, Juan-Esteban Suarez Cardona, Ivo F. Sbalzarini · 4 août 2026

  We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain. This Push-Forward Transform enables invariant and robust comparison of shapes, preserving intrinsic geometric information. Quantitatively comparing shapes and the…

• SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp

  Nawazish Khan, Sanjay Bhandari, Sarang Joshi, Alzbeta Novotna, Tiffany Jeong, Loretta Bowman, Michael Hernandez, Tobi Somorin, Viraj Govani, Jesse Glodstein, Shireen Elhabian · 4 août 2026

  Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces. Real-world clinical photogrammetry scans are often noisy, partial, and cluttered, hindering the adoption of radiation-free su…

• FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

  Wenzhe He, Meng Wang, JiaWei Qian, Jinfeng Xu, Ying Liu, Ruihui Li · 30 juillet 2026

  Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion. During training, noisy point clouds are constructed by perturbing complete ground-truth scenes, whereas during inference, they are initialized by adding noise to duplicated partial scans. This…

• Neural Representation of Minimal Surfaces

  Jiayin Sun, Albert Chern · 28 juillet 2026

  We propose a neural representation for minimal surfaces. Unlike prior approaches based on discretization or Physics-Informed Neural Networks (PINNs), where meshes or neural fields are optimized to approximate the governing equations, our method builds on an exact representation, similar to the class…

• SM4RT: Learning Structured Motion Geometry for 4D Reconstruction

  Shing Ho J. Lin, Wenzhao Zheng, Dong Zhuo, Yuqi Wu, Jie Zhou, Jiwen Lu · 27 juillet 2026

  Geometry Foundation Models (GFMs) have substantially advanced monocular 3D reconstruction, yet extending this capability to 4D dynamic understanding remains a fundamental challenge. Most existing motion perception methods (e.g., sparse tracking, dense point-wise flow) treat motion as independent poi…

• Masked Topology Modeling for Self-Supervised Learning on Parametric CAD

  Heinrich Jiang, Jennifer Jang · 24 juillet 2026

  Computer aided design (CAD) is ubiquitous: virtually any modern object was designed using editable CAD tools. However, with the shortage of available CAD datasets in its native editable and parametric format, boundary representation (B-Rep), it is ever more important to develop data-efficient method…

• PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics

  Haocheng Yin, Shuohan Tao, Yongsheng Chen, Lu Gan · 24 juillet 2026

  Predicting how deformable objects evolve under robotic manipulation is a longstanding challenge. Existing approaches typically rely on per-object optimization to fit material parameters, which can be slow and cannot generalize, while end-to-end learned alternatives extrapolate poorly and often viola…

• Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration

  Xiaoye Zhu, Weixin Li, Junan Huo, Bozhong Wang, Jia Zeng, Yi Yang, Cen Chen, Qi Liu · 21 juillet 2026

  A fundamental intent asymmetry plagues modern 3D asset creation: while state-of-the-art 3D toolchains demand precise, executable parameters, ordinary users typically provide vague, underspecified instructions. Current 3D agents treat this ambiguity as noise, defaulting to blind execution under a sin…

• OV-MAP: Open-Vocabulary Zero-Shot 3D Instance Segmentation Map for Robots

  Juno Kim, Yesol Park, Hye-Jung Yoon, Byoung-Tak Zhang · 21 juillet 2026

  We introduce OV-MAP, a novel approach to open-world 3D mapping for mobile robots by integrating open-features into 3D maps to enhance object recognition capabilities. A significant challenge arises when overlapping features from adjacent voxels reduce instance-level precision, as features spill over…

• Cluster-Aware Matching via Laplacian Optimal Transport

  Gabriel Samberg, YoonHaeng Hur, Yuehaw Khoo, Nir Sharon · 20 juillet 2026

  In many applications of matching, the point clouds to be matched are not merely unstructured sets of points but rather samples from distributions with an intrinsic cluster structure. In such cases, as individual points are often interchangeable within a coherent region, finding a robust region-to-re…

• E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding

  Chankyo Kim, Maani Ghaffari · 20 juillet 2026

  3D Gaussian Splatting (3DGS) captures scenes by coupling explicit geometry (position, covariance) with view-dependent photometry (Spherical Harmonics). However, building $\mathrm{SE}(3)$-equivariant architectures on these primitives presents a fundamental representation bottleneck. Color has been tr…

• HyperShadow: A Benchmark for Detecting 3D Projections of Higher-Dimensional Spatial Objects

  Akshay Sasi · 17 juillet 2026

  Machine-learning datasets labelled "4D" universally denote three spatial dimensions plus time. We introduce HyperShadow, the first public benchmark in which the fourth, fifth, and sixth dimensions are spatial: the task is to decide whether a 3D point cloud is a native three-dimensional shape or the …

• Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding

  Tam Thuc Do, Philip A. Chou, Gene Cheung · 16 juillet 2026

  Given encoded 3D point cloud geometry available at the decoder, we study the problem of lossy attribute compression in a multi-resolution B-spline projection framework. A target continuous 3D attribute function is first projected onto a sequence of nested subspaces $\mathcal{F}^{(p)}_{l_0} \subseteq…

• Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability

  Everest Yang, Skye Thompson, George D. Konidaris · 16 juillet 2026

  Surgical tissue retraction requires effective manipulation planning under partial and noisy perception. We study state estimation for deformable tissue retraction, where only sparse observations of the tissue surface are available at decision time. We propose a learned state estimator that reconstru…

• Toward Inclusive Avatar Design with Limb Differences Through Artificial Intelligence

  Fernanda Miyuki Yamada, Jo\~ao Paulo Gois, Hiroki Takahashi · 14 juillet 2026

  As extended reality becomes more popular for social interaction and entertainment, 3D avatars must represent the full diversity of body types. Most 3D avatar systems only support normative bodies and do not accurately depict people with limb differences, amputations, or other morphological variation…

• Annotation-Free Furniture Codes: What They Encode, and How Far They Transfer

  Benjamin Friedman · 14 juillet 2026

  Layout-based 3D scene synthesizers place each object using two human-annotated channels: a categorical class label and a canonical-pose convention. We ask whether a single self-supervised token derived from object geometry can replace both, and study such tokens directly as a representation, decoupl…

• PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations

  Weiheng Zhong, Jing Bi, Victor Oancea, Hadi Meidani · 10 juillet 2026

  While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by high memory consumption and the single node bottleneck, where the maximum processable mesh resolution is strictly limited by the VRAM of a single co…

• Intrinsic Green's Learning: Supervised Learning on Manifolds via Inverse PDE

  Alexandre Quemy · 9 juillet 2026

  We introduce Intrinsic Green's Learning (IGL), a framework that models a target function on a manifold as the solution to a linear PDE whose source term is learned from data. Rather than approximating the target directly, IGL learns a source and integrates it against a Green's kernel. An encoder dis…

• Intrinsic Green's Learning: Supervised Learning on Manifolds via Inverse PDE

  Alexandre Quemy · 9 juillet 2026

  We introduce Intrinsic Green's Learning (IGL), a framework that models a target function on a manifold as the solution to a linear PDE whose source term is learned from data. Rather than approximating the target directly, IGL learns a source and integrates it against a Green's kernel. An encoder dis…

• SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts

  Haida Feng, Hao Wei, Haolin Wang, Shiwei Li, Chade Li, Yihong Wu · 9 juillet 2026

  Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry. Existing 3D-aware models either rely on costly 3D-specific data or utilize RGB-only inputs with heuristic sampling and monolithic, shallow fusion, wh…

• Foundation Models for Automatic CAD Generation

  J de Curt\`o, Victoria Guill\'en, I. de Zarz\`a · 8 juillet 2026

  Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-language specifications. This chapter presents an empirical study of foundation models for automatic Computer-Aided Design (CAD) generation of mecha…