Advancing Structured Priors for Sparse-Voxel Surface Reconstruction

• URL: http://arxiv.org/abs/2601.17720v1
• Date: Sun, 25 Jan 2026 06:49:22 GMT
• Title: Advancing Structured Priors for Sparse-Voxel Surface Reconstruction
• Authors: Ting-Hsun Chi, Chu-Rong Chen, Chi-Tun Hsu, Hsuan-Ting Lin, Sheng-Yu Huang, Cheng Sun, Yu-Chiang Frank Wang,
• Abstract summary: Two promising explicit representations, 3D Gaussian Splatting and sparse-voxelization, exhibit complementary strengths and weaknesses.<n>We combine the advantages of both by a voxel method that places voxels at plausible locations and with appropriate levels of detail.<n>Experiments on standard benchmarks demonstrate improvements over prior methods in accuracy, better fine-structure recovery, and more complete surfaces.
• Score: 38.315369778574386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing accurate surfaces with radiance fields has progressed rapidly, yet two promising explicit representations, 3D Gaussian Splatting and sparse-voxel rasterization, exhibit complementary strengths and weaknesses. 3D Gaussian Splatting converges quickly and carries useful geometric priors, but surface fidelity is limited by its point-like parameterization. Sparse-voxel rasterization provides continuous opacity fields and crisp geometry, but its typical uniform dense-grid initialization slows convergence and underutilizes scene structure. We combine the advantages of both by introducing a voxel initialization method that places voxels at plausible locations and with appropriate levels of detail, yielding a strong starting point for per-scene optimization. To further enhance depth consistency without blurring edges, we propose refined depth geometry supervision that converts multi-view cues into direct per-ray depth regularization. Experiments on standard benchmarks demonstrate improvements over prior methods in geometric accuracy, better fine-structure recovery, and more complete surfaces, while maintaining fast convergence.

Related papers

• GVGS: Gaussian Visibility-Aware Multi-View Geometry for Accurate Surface Reconstruction [15.170414649311441]
  3D Gaussian Splatting enables efficient optimization and high-quality rendering, yet accurate surface reconstruction remains challenging.<n>We introduce a visibility-aware multi-view geometric consistency constraint that aggregates the visibility of shared Gaussian primitives across views.<n>We also propose a progressive quadtree-calibrated Monocular depth constraint that performs block-wise affine calibration from coarse to fine spatial scales.
  arXiv  Detail & Related papers  (2026-01-28T07:48:51Z)
• SVRecon: Sparse Voxel Rasterization for Surface Reconstruction [60.92372415355283]
  We extend the recently proposed sparse voxelization paradigm to the task of high-fidelity surface reconstruction by integrating SVRecon.<n>Our method achieves strong reconstruction accuracy while having consistently speedy convergence.
  arXiv  Detail & Related papers  (2025-11-21T16:32:01Z)
• SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction [26.59203606048875]
  We propose net, a method that reconstructs more accurate and detailed surfaces while preserving high-quality novel view rendering.<n>Our key insight is to introduce Stereo Geometry-Texture Alignment, which bridges rendering quality and geometry estimation.<n>In addition, we present a Pseudo-Feature Enhanced Geometry Consistency that enforces multi-view geometric consistency.
  arXiv  Detail & Related papers  (2025-11-18T16:24:37Z)
• SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction [8.905668938519279]
  3D Gaussian representations have significantly improved the quality and efficiency of image-based scene reconstruction.<n>Many existing methods rely on approximate depth estimates and global sortings, which can introduce artifacts and limit the fidelity of the reconstructed mesh.<n>We present Sorted Opacity Fields (SOF), a method designed to recover detailed surfaces from 3D Gaussians with both speed and precision.
  arXiv  Detail & Related papers  (2025-06-23T21:20:52Z)
• QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization [69.50126552763157]
  Surface reconstruction is fundamental to computer vision and graphics, enabling applications in 3D modeling, mixed reality, robotics, and more.<n>Existing approaches based on rendering obtain promising results, but optimize on a per-scene basis, resulting in a slow optimization that can struggle to model textureless regions.<n>We introduce QuickSplat, which learns data-driven priors to generate dense initializations for 2D gaussian splatting optimization of large-scale indoor scenes.
  arXiv  Detail & Related papers  (2025-05-08T18:43:26Z)
• Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views [45.125032766506536]
  We propose Sparse2DGS, an MVS-d Gaussian Splatting pipeline for complete and accurate reconstruction.<n>Our key insight is to incorporate the geometric-prioritized enhancement schemes, allowing for direct and robust geometric learning under ill-posed conditions.<n>Sparse2DGS outperforms existing methods by notable margins while being $2times$ faster than the NeRF-based fine-tuning approach.
  arXiv  Detail & Related papers  (2025-04-29T02:47:02Z)
• FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction [50.534213038479926]
  FreeSplat++ is an alternative approach to large-scale indoor whole-scene reconstruction.<n>Our method with depth-regularized per-scene fine-tuning demonstrates substantial improvements in reconstruction accuracy and a notable reduction in training time.
  arXiv  Detail & Related papers  (2025-03-29T06:22:08Z)
• GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
  GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.<n>Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
  arXiv  Detail & Related papers  (2024-11-29T03:54:54Z)
• GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction [5.112375652774415]
  We propose a unified optimization framework that integrates neural signed distance fields (SDFs) with 3DGS for accurate geometry reconstruction and real-time rendering.<n>Our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-05-30T03:46:59Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)

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.