3D Shape Modeling and Analysis

Neueste Paper

• Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport

  Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante · 6. April 2026

  Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Classical approaches often rely on feature concatenation or explicit alignment assumptions, which become restrictive under heterogeneous geometries or no…

• VoxelCodeBench: Benchmarking 3D World Modeling Through Code Generation

  Yan Zheng, Florian Bordes · 6. April 2026

  Evaluating code generation models for 3D spatial reasoning requires executing generated code in realistic environments and assessing outputs beyond surface-level correctness. We introduce a platform VoxelCode, for analyzing code generation capabilities for 3D understanding and environment creation. …

• Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding

  Ye Mao, Weixun Luo, Ranran Huang, Junpeng Jing, Krystian Mikolajczyk · 6. April 2026

  Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable representations for 3D scene understanding. In this paper, we propose UniScene3D, a transformer-based encoder that learns unified scene representations f…

• A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis

  Zelong Bi, Pierre Lafaye de Micheaux · 2. April 2026

  The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article prov…

• Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

  Jorge Condor, Nicolas Moenne-Loccoz, Merlin Nimier-David, Piotr Didyk, Zan Gojcic, Qi Wu · 2. April 2026

  Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstruction tasks. Compared to neural fields, these representations are more flexible, adaptive, and scale better to large scenes. However, the limited expressivity…

• WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation

  Amogh Joshi, Julian Ost, Felix Heide · 1. April 2026

  Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method capable of generating unbounded 3D worlds. Building upon a foundational property of flow matching - namely, defining a …

• GENIE: Gram-Eigenmode INR Editing with Closed-Form Geometry Updates

  Samundra Karki, Adarsh Krishnamurthy, Baskar Ganapathysubramanian · 1. April 2026

  Implicit Neural Representations (INRs) provide compact models of geometry, but it is unclear when their learned shapes can be edited without retraining. We show that the Gram operator induced by the INR's penultimate features admits deformation eigenmodes that parameterize a family of realizable edi…

• LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics

  Sarah Katz, Francesco Romor, Jia-Jie Zhu, Alfonso Caiazzo · 31. März 2026

  We introduce a novel conditional stochastic interpolant framework for generative modeling of three-dimensional shapes. The method builds on a recent LDDMM-based registration approach to learn the conditional drift between geometries. By leveraging the resulting pull-back and push-forward operators, …

• Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders

  Josefina Catoni, Domonkos Martos, Ferenc Csikor, Enzo Ferrante, Diego H. Milone, Bal\'azs Mesz\'ena, Gerg\H{o} Orb\'an, Rodrigo Echeveste · 31. März 2026

  Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), have the potential to meet this demand by building latent representations that learn to associate uncertainties with infere…

• Generative Shape Reconstruction with Geometry-Guided Langevin Dynamics

  Linus H\"arenstam-Nielsen, Dmitrii Pozdeev, Thomas Dag\`es, Nikita Araslanov, Daniel Cremers · 31. März 2026

  Reconstructing complete 3D shapes from incomplete or noisy observations is a fundamentally ill-posed problem that requires balancing measurement consistency with shape plausibility. Existing methods for shape reconstruction can achieve strong geometric fidelity in ideal conditions but fail under rea…

• Neural Approximation of Generalized Voronoi Diagrams

  Panagiotis Rigas, George Ioannakis, Ioannis Emiris · 31. März 2026

  We introduce VoroFields, a hierarchical neural-field framework for approximating generalized Voronoi diagrams of finite geometric site sets in low-dimensional domains under arbitrary evaluable point-to-site distances. Instead of constructing the diagram combinatorially, VoroFields learns a continuou…

• GLASS: Geometry-aware Local Alignment and Structure Synchronization Network for 2D-3D Registration

  Zhixin Cheng, Jiacheng Deng, Xinjun Li, Bohao Liao, Li Liu, Xiaotian Yin, Baoqun Yin, Tianzhu Zhang · 30. März 2026

  Image-to-point cloud registration methods typically follow a coarse-to-fine pipeline, extracting patch-level correspondences and refining them into dense pixel-to-point matches. However, in scenes with repetitive patterns, images often lack sufficient 3D structural cues and alignment with point clou…

• Meta-Learned Adaptive Optimization for Robust Human Mesh Recovery with Uncertainty-Aware Parameter Updates

  Shaurjya Mandal, Nutan Sharma, John Galeotti · 30. März 2026

  Human mesh recovery from single images remains challenging due to inherent depth ambiguity and limited generalization across domains. While recent methods combine regression and optimization approaches, they struggle with poor initialization for test-time refinement and inefficient parameter updates…

• ExtrinSplat: Decoupling Geometry and Semantics for Open-Vocabulary Understanding in 3D Gaussian Splatting

  Jiayu Ding, Xinpeng Liu, Zhiyi Pan, Shiqiang Long, Ge Li · 30. März 2026

  Lifting 2D open-vocabulary understanding into 3D Gaussian Splatting (3DGS) scenes is a critical challenge. Mainstream methods, built on an embedding paradigm, suffer from three key flaws: (i) geometry-semantic inconsistency, where points, rather than objects, serve as the semantic basis, limiting se…

• GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation

  Xujing Tao, Chuxin Wang, Yubo Ai, Zhixin Cheng, Zhuoyuan Li, Liangsheng Liu, Yujia Chen, Xinjun Li, Qiao Li, Wenfei Yang, Tianzhu Zhang · 30. März 2026

  Open-vocabulary 3D semantic segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-vocabulary models. However, aligning 3D features to the 2D representation space restricts intrinsic 3D geometric learning an…

• From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion

  D\'avid Pukanec, Tibor Kub\'ik, Michal \v{S}pan\v{e}l · 30. März 2026

  We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth. Building upon recent advancements in conditioned diffusion models for 3D shapes, we developed a model capable of an automated tooth crown completion conditi…

• CADSmith: Multi-Agent CAD Generation with Programmatic Geometric Validation

  Jesse Barkley, Rumi Loghmani, Amir Barati Farimani · 30. März 2026

  Existing methods for text-to-CAD generation either operate in a single pass with no geometric verification or rely on lossy visual feedback that cannot resolve dimensional errors. We present CADSmith, a multi-agent pipeline that generates CadQuery code from natural language. It then undergoes an ite…

• Exact Risk Curves of signSGD in High-Dimensions: Quantifying Preconditioning and Noise-Compression Effects

  Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette · 27. März 2026

  In recent years, signSGD has garnered interest as both a practical optimizer as well as a simple model to understand adaptive optimizers like Adam. Though there is a general consensus that signSGD acts to precondition optimization and reshapes noise, quantitatively understanding these effects in the…

• Few TensoRF: Enhance the Few-shot on Tensorial Radiance Fields

  Thanh-Hai Le, Hoang-Hau Tran, Trong-Nghia Vu · 27. März 2026

  This paper presents Few TensoRF, a 3D reconstruction framework that combines TensorRF's efficient tensor based representation with FreeNeRF's frequency driven few shot regularization. Using TensorRF to significantly accelerate rendering speed and introducing frequency and occlusion masks, the method…

• Flow matching on homogeneous spaces

  Francesco Ruscelli · 27. März 2026

  We propose a general framework to extend Flow Matching to homogeneous spaces, i.e. quotients of Lie groups. Our approach reformulates the problem as a flow matching task on the underlying Lie group by lifting the data distributions. This strategy avoids the potentially complicated geometry of homoge…

• UniQueR: Unified Query-based Feedforward 3D Reconstruction

  Chensheng Peng, Quentin Herau, Jiezhi Yang, Yichen Xie, Yihan Hu, Wenzhao Zheng, Matthew Strong, Masayoshi Tomizuka, Wei Zhan · 25. März 2026

  We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT, and AnySplat typically predict per-pixel point maps or pixel-aligned Gaussians, which remain fundamentally 2.5D and limi…

• PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning

  Yuanhang Lei, Tao Cheng, Xingxuan Li, Boming Zhao, Siyuan Huang, Ruizhen Hu, Peter Yichen Chen, Hujun Bao, Zhaopeng Cui · 25. März 2026

  Achieving real-time physics-based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics-informed framework that addresses this challenge. In the spirit of Linear Blend Skinning, we learn continuous skinning fields as…

• Sinkhorn Based Associative Memory Retrieval Using Spherical Hellinger Kantorovich Dynamics

  Aratrika Mustafi, Soumya Mukherjee · 24. März 2026

  We propose a dense associative memory for empirical measures (weighted point clouds). Stored patterns and queries are finitely supported probability measures, and retrieval is defined by minimizing a Hopfield-style log-sum-exp energy built from the debiased Sinkhorn divergence. We derive retrieval d…

• GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion

  Ruiqi Xian, Jing Liang, He Yin, Xuewei Qi, Dinesh Manocha · 24. März 2026

  We present \emph{GaussianSSC}, a two-stage, grid-native and triplane-guided approach to semantic scene completion (SSC) that injects the benefits of Gaussians without replacing the voxel grid or maintaining a separate Gaussian set. We introduce \emph{Gaussian Anchoring}, a sub-pixel, Gaussian-weight…

• VoroLight: Learning Voronoi Surface Meshes via Sphere Intersection

  Jiayin Lu, Ying Jiang, Yumeng He, Yin Yang, Chenfanfu Jiang · 24. März 2026

  Voronoi diagrams naturally produce convex, watertight, and topologically consistent cells, making them an appealing representation for 3D shape reconstruction. However, standard differentiable Voronoi approaches typically optimize generator positions in stable configurations, which can lead to local…