Voxel Deformation-Aware Neural Intersection Function
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Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We extend the Locally-Subdivided Neural Intersection Function (LSNIF) to support parameterized deformable and animated geometry. Our approach introduces a rest-space and deformed-space formulation inspired by meshless rendering, allowing ray samples to be mapped back to a canonical space where a single neural network represents geometry consistently across poses without retraining. To maintain accuracy under deformation-aware training, we incorporate scale-invariant distance regression, uncertainty-weighted multi-task learning, and a hybrid positional-grid encoding. The resulting method preserves the compactness and efficiency of LSNIF while enabling robust neural intersection prediction for dynamic geometry.
Description
@inproceedings{10.2312:egs.20261026,
booktitle = {Eurographics 2026 - Short Papers},
editor = {},
title = {{Voxel Deformation-Aware Neural Intersection Function}},
author = {Kao, Chih-Chen and Makowski, Grzegorz and Fujieda, Shin and Harada, Takahiro},
year = {2026},
publisher = {The Eurographics Association},
ISSN = {2309-5059},
ISBN = {978-3-03868-299-8},
DOI = {10.2312/egs.20261026}
}