In this section
@inproceedings{Qiao:2026:10.1007/978-3-032-05325-1_33,
author = {Qiao, M and Zheng, J and Zhang, W and Ma, Q and Li, L and Kainz, B and O’Regan, DP and Matthews, PM and Niederer, S and Bai, W},
doi = {10.1007/978-3-032-05325-1_33},
pages = {343--353},
title = {Mesh4D: A Motion-Aware Multi-view Variational Autoencoder for 3D+t Mesh Reconstruction},
url = {http://dx.doi.org/10.1007/978-3-032-05325-1_33},
year = {2026}
}
TY - CPAPER
AB - Reconstructing temporally coherent 3D meshes of the beating heart from multi-view MR images is an important but challenging problem. The challenge is entangled by the complexity in integrating multi-view data, the sparse coverage of a 3D geometry by 2D image slices, and the interplay between geometry and motion. Current approaches often treat mesh reconstruction and motion estimation as two separate problems. Here we propose Mesh4D, a novel motion-aware method that jointly learns cardiac shape and motion, directly from multi-view MR image sequences. The method introduces three key innovations: (1) A cross-attention encoder that fuses multi-view image information, (2) A transformer-based variational autoencoder (VAE) that jointly model the image feature and motion, and (3) A deformation decoder that generates continuous deformation fields and temporally smooth 3D+t cardiac meshes. Incorporating geometric regularisation and motion consistency constraints, Mesh4D can reconstruct high-quality 3D+t meshes (7,698 vertices, 15,384 faces) of the heart ventricles across 50 time frames, within less than 3 s. When compared to existing approaches, Mesh4D achieves notable improvements in reconstruction accuracy and motion smoothness, offering an efficient image-to-mesh solution for quantifying shape and motion of the heart and creating digital heart models.
AU - Qiao,M
AU - Zheng,J
AU - Zhang,W
AU - Ma,Q
AU - Li,L
AU - Kainz,B
AU - O’Regan,DP
AU - Matthews,PM
AU - Niederer,S
AU - Bai,W
DO - 10.1007/978-3-032-05325-1_33
EP - 353
PY - 2026///
SN - 0302-9743
SP - 343
TI - Mesh4D: A Motion-Aware Multi-view Variational Autoencoder for 3D+t Mesh Reconstruction
UR - http://dx.doi.org/10.1007/978-3-032-05325-1_33
ER -