Fast Voxelization and Level of Detail for Microgeometry Rendering
- URL: http://arxiv.org/abs/2604.13191v1
- Date: Tue, 14 Apr 2026 18:16:15 GMT
- Title: Fast Voxelization and Level of Detail for Microgeometry Rendering
- Abstract summary: Many materials show anisotropic light scattering patterns due to the shape and local alignment of their underlying micro structures.<n>The acquisition of voxel data from such objects is a time and memory-intensive task.<n>Most rendering approaches require an additional Level-of-Detail (LoD) data structure to aggregate the visual appearance.
- Score: 2.408299705177128
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many materials show anisotropic light scattering patterns due to the shape and local alignment of their underlying micro structures: surfaces with small elements such as fibers, or the ridges of a brushed metal, are very sparse and require a high spatial resolution to be properly represented as a volume. The acquisition of voxel data from such objects is a time and memory-intensive task, and most rendering approaches require an additional Level-of-Detail (LoD) data structure to aggregate the visual appearance, as observed from multiple distances, in order to reduce the number of samples computed per pixel (E.g.: MIP mapping). In this work we introduce first, an efficient parallel voxelization method designed to facilitate fast data aggregation at multiple resolution levels, and second, a novel representation based on hierarchical SGGX clustering that provides better accuracy than baseline methods. We validate our approach with a CUDA-based implementation of the voxelizer, tested both on triangle meshes and volumetric fabrics modeled with explicit fibers. Finally, we show the results generated with a path tracer based on the proposed LoD rendering model.
Related papers
- Shape Representation using Gaussian Process mixture models [2.7882300801098037]
We propose a novel, object-specific functional shape representation that models surface geometry with Gaussian Process (GP) mixture models.<n>Our method is lightweight, leveraging GPs to learn continuous directional distance fields from sparsely sampled point clouds.
arXiv Detail & Related papers (2026-04-01T13:11:12Z) - Robust Multi-Manifold Clustering via Simplex Paths [10.304857373037596]
This article introduces a novel, geometric approach for multi-manifold clustering (MMC)<n>We first compute a locality graph on d-simplices, using the dihedral angle in between adjacent simplices as the graph weights, and then compute infinity path distances in this simplex graph.<n>We analyze the properties of LAPD under random sampling, and prove that with an appropriate denoising procedure, this metric separates the manifold components with high probability.
arXiv Detail & Related papers (2025-07-14T18:22:50Z) - SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling [79.56581753856452]
SparseFlex is a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to $10243$ directly from rendering losses.<n>By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
arXiv Detail & Related papers (2025-03-27T17:46:42Z) - ProbeSDF: Light Field Probes for Neural Surface Reconstruction [4.0130618054041385]
SDF-based differential rendering frameworks have achieved state-of-the-art multiview 3D shape reconstruction.<n>We re-examine this family of approaches by minimally reformulating its core appearance model.<n>We show this performance to be consistently achieved on real data over two widely different and popular application fields.
arXiv Detail & Related papers (2024-12-13T12:18:26Z) - Volumetric Surfaces: Representing Fuzzy Geometries with Layered Meshes [59.17785932398617]
High-quality view synthesis relies on volume rendering, splatting, or surface rendering.<n>We present a novel representation for real-time view synthesis where the number of sampling locations is small and bounded.<n>We achieve this by representing objects as semi-transparent multi-layer meshes rendered in a fixed order.
arXiv Detail & Related papers (2024-09-04T07:18:26Z) - Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering [13.638434337947302]
FSSMSC is a novel solution to the high computational complexity commonly found in existing approaches.
The method generates a consensus anchor graph across all views, representing each data point as a sparse linear combination of chosen landmarks.
The effectiveness and efficiency of FSSMSC are validated through extensive experiments on multiple benchmark datasets of varying scales.
arXiv Detail & Related papers (2024-08-11T06:54:00Z) - GPU-Accelerated RSF Level Set Evolution for Large-Scale Microvascular Segmentation [2.5003043942194236]
We propose a reformulation and implementation of the region-scalable fitting (RSF) level set model.
This makes it amenable to three-dimensional evaluation using both single-instruction multiple data (SIMD) and single-program multiple-data (SPMD) parallel processing.
We tested this 3D parallel RSF approach on multiple data sets acquired using state-of-the-art imaging techniques to acquire microvascular data.
arXiv Detail & Related papers (2024-04-03T15:37:02Z) - T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified
Visual Modalities [69.16656086708291]
Diffusion Probabilistic Field (DPF) models the distribution of continuous functions defined over metric spaces.
We propose a new model comprising of a view-wise sampling algorithm to focus on local structure learning.
The model can be scaled to generate high-resolution data while unifying multiple modalities.
arXiv Detail & Related papers (2023-05-24T03:32:03Z) - Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids [84.90863397388776]
We propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without distances.
Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data.
Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
arXiv Detail & Related papers (2023-05-22T16:50:19Z) - Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering [84.37776381343662]
Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information.
We propose mip voxel grids (Mip-VoG), an explicit multiscale representation for real-time anti-aliasing rendering.
Our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously.
arXiv Detail & Related papers (2023-04-20T04:05:22Z) - Real-Time Scene Text Detection with Differentiable Binarization and
Adaptive Scale Fusion [62.269219152425556]
segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field.
We propose a Differentiable Binarization (DB) module that integrates the binarization process into a segmentation network.
An efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively.
arXiv Detail & Related papers (2022-02-21T15:30:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.