Statistical Shape Modeling

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5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 13

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Shape-Bayes: Bayesian Inference of Structured Shapes under Visual Ambiguity
Oct 6, 2026Mani Kumar Tellamekala, Tosh Brown, Michel ValstarBayesian Inference Uncertainty Quantification
Amsterdam, The Netherlands · BlueSkeye AI · University of Nottingham, U.K.
Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease
Oct 4, 2026Olakunle S. Abawonse, Fatou FallAlzheimer's Disease Statistical Shape Modeling
African Institute for Mathematical Sciences (AIMS), Rwanda · African Institute for Mathematical Sciences (AIMS), Senegal
An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients
Sep 28, 2026Zixi Yi, Limeng Qu, Gary P. T. ChoiBreast Cancer Statistical Shape Modeling
School of Mathematics and Statistics, Central South University, Changsha, Hunan, China · Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China · Clinical Research Center for Breast Disease in Hunan Province, Changsha, Hunan, China +1
NeuralSRNF: Neural Square Root Normal Fields for the Statistical Shape Analysis and Generation of Nonrigid 3D and 4D Objects
Sep 23, 2026Awais Nizamani, Hamid Laga, Guanjin Wang +3Neural Representation Geometry Statistical Shape Modeling
Murdoch University, Perth, Australia · The University of Western Australia, Perth, Australia · The Johns Hopkins University, Maryland, US
Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
Sep 9, 2026Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata +23D Gaussian Splatting 3D Medical Imaging
Polytechnique Montréal, Canada · Mila - Quebec AI Institute, Canada · CHUM - University of Montreal Hospital, Canada
A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction
Sep 8, 2026Florence Dell'Aniello Picard, Pranav Poudel, Nairouz Shehata +23D Reconstruction 3D Medical Imaging
Polytechnique Montréal, Canada · Mila - Quebec AI Institute, Canada · CHUM - University of Montreal Hospital, Canada
Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces
Aug 7, 2026Hengchao Chen, Yuanyao Tan, Chao Huang +2Clustering Statistical Shape Modeling
University of Toronto · Florida State University · Eli Lilly and Company +3
SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
Jul 31, 2026Nawazish Khan, Sanjay Bhandari, Sarang Joshi +8Point Cloud Learning Statistical Shape Modeling
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA · Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112, USA · Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA +1
Deep Shape Regression for Planar Curves with Multimodal Covariates
Jul 21, 2026Manuel Pfeuffer, Roshan Prakash Rane, Hadya Yassin +2Statistical Shape Modeling Covariance Estimation
Humboldt-Universität zu Berlin, Berlin, Germany · Hertie Institute for AI in Brain Health, Universität Tübingen, Tübingen, Germany · Universität Tübingen, Tübingen, Germany +1
TreeSRNF: Square-Root Normal Fields for Generative Modelling of the Geometric and Structural Variability in Tree-like 3D Objects
Jul 15, 2026Tahmina Khanam, Hamid Laga, Mohammed Bennamoun +4Statistical Shape Modeling Non-Rigid Shape Correspondence
Murdoch University, WA, Australia. · University of Western Australia, WA, Australia. · Johns Hopkins University, USA.
From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Jun 29, 2026Francesco Fabbri, Martino Andrea Scarpolini, Paolo Ciancarella +43D Mesh Generation Statistical Shape Modeling
Gran Sasso Science Institute (GSSI), L’Aquila, Italy. · European Hospital, Rome, Italy. · University of Edinburgh, Edinburgh, Scotland. +2
Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
Jun 15, 2026Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin +6Statistical Shape Modeling Geometric Representation Learning
Department of Mathematics, The Chinese University of Hong Kong, Hong Kong SAR · Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada · Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel +6
Statistical Hand Shape Modeling from Clinical CT Scans Using Deep Learning and Implicit Skinning
May 16, 2026Gokce Guven, Hasan Fehmi Ates, Deniz Karasahin +1Statistical Shape Modeling
Dept. of Computer Science and Engineering Özyeğgin University İstanbul, Türkiye · Dept. of Artificial Intelligence and Data Engineering Özyeğgin University İstanbul, Türkiye · Osteoid Inc. İstanbul, Türkiye

Oct 6, 2026, cs.CVJ/K move · Enter open · S save

Shape-Bayes: Bayesian Inference of Structured Shapes under Visual Ambiguity

Mani Kumar Tellamekala, Tosh Brown, Michel Valstar

Amsterdam, The Netherlands · BlueSkeye AI · University of Nottingham, U.K.

Bayesian Inference Uncertainty Quantification Statistical Shape Modeling

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Perceiving structured shapes, such as human faces, from pixels is an inherently ambiguous task in real-world conditions. Yet, shape inference is largely posed as a deterministic regression task predicting fixed spatial coordinates. We find that deterministic regression is brittle when visual evidence is ambiguous or incomplete; under severe occlusions deterministic models exhibit structural collapse, predicting incoherent shapes or reverting to generic averages. To address this, we introduce Shape-Bayes, a probabilistic framework that couples uncertainty-aware visual perception with Bayesian shape reasoning. Rather than forcing point estimates, Shape-Bayes dynamically weights visual evidence against geometric priors to infer a structurally valid shape posterior. Demonstrated on human face shape regression, a rigorous testbed featuring complex non-rigid deformations and strict anatomical constraints, Shape-Bayes comprises: (1) a base model predicting noisy landmarks alongside distilled aleatoric uncertainties; (2) a lightweight Transformer encoding these observations into an adaptive prior over a PCA shape manifold; and (3) a differentiable Bayesian solver computing closed-form posteriors by balancing the noisy predictions against this prior. By guaranteeing complete structural integrity, Shape-Bayes achieves an absolute improvement of up to ~34% IDR over state-of-the-art deterministic models. Simultaneously, it yields highly calibrated uncertainty bounds and reduces relative error by up to 12.5%, establishing a new state-of-the-art for robust 2D face shape regression under severe occlusion. The project page is at https://shape-bayes.github.io.

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