The paper introduces SVRecon, a generalizable neural surface reconstruction framework that uses sparse volumetric representations to achieve high-resolution 3D reconstruction. It employs a two-stage architecture: first predicting occupied voxels with an occupancy network, then rendering only within those regions using specialized sparse algorithms. This approach allows reconstruction at resolutions up to 512³ on 32 GB hardware, producing smoother and more precise surfaces, especially in sparse-view scenarios.
By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Ming Xu, Hieu Le, Pascal Fua
arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.
By Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben, Steve Petruzza, Qi Wu, Will Usher, Sidharth Kumar
arXiv:2608.23869v1 Announce Type: new
Abstract: While state-of-the-art generative models produce high-fidelity 3D meshes, these outputs lack the physical properties required for interactive simulatio...
By Mauro Comi, Jordi Serrano Berbel, Kevis-Kokitsi Maninis, Philipp Henzler, Manuel Sanchez
The paper introduces SPAR3S, a sparse voxel‑aligned 3D latent generative model that completes 3D scenes from sparse, unconstrained multi‑view images. By representing only occupied voxels in a compact latent space and training a masked autoregressive transformer with photometric supervision via differentiable 3D Gaussian Splatting, the method predicts missing latent tokens and spatial support, enabling efficient and spatially consistent generation of unseen regions. Experiments on synthetic indoor scenes and RealEstate10k demonstrate higher novel‑view quality and real‑world applicability compared to prior work.
arXiv:2610.00188v1 Announce Type: cross
Abstract: Voxel-based volumetric mapping is fundamental to 3D reconstruction, yet fixed-resolution grids remain inherently inefficient - wasting memory in unif...
By Alpay Ozkan, Tunc Ozan Aydin, Marc Pollefeys, Jelena Trisovic, Daniel Barath
RAFT-DVC is a resolution‑aware family of recurrent all‑pairs field transform (RAFT) based digital volume correlation (DVC) solvers that use encoder downsampling factors of 2, 4, and 8. The solvers localize displacement to about 0.017 feature‑grid voxels, with raw‑volume error scaling roughly as 0.017 s voxels, and exhibit complementary operating regimes determined by displacement reach and volumetric‑texture compatibility. Synthetic benchmarks show comparable performance to tuned classical DVC for fine‑texture, small‑to‑moderate displacements, while outperforming it for coarse‑texture, large‑displacement scenarios; additional tests on confocal and micro‑CT images confirm the importance of matching solver regimes to deformation magnitude and texture, and demonstrate cross‑texture transfer and improved accuracy after correcting sampler geometry.
By Zixiang Tong, Lehu Bu, Jin Yang