Neural Shell Texture Splatting: More Details and Fewer Primitives
- URL: http://arxiv.org/abs/2507.20200v1
- Date: Sun, 27 Jul 2025 09:39:10 GMT
- Title: Neural Shell Texture Splatting: More Details and Fewer Primitives
- Authors: Xin Zhang, Anpei Chen, Jincheng Xiong, Pinxuan Dai, Yujun Shen, Weiwei Xu,
- Abstract summary: We introduce a neural shell texture, a global representation that encodes texture information around the surface.<n>Our evaluation demonstrates that this disentanglement enables high parameter efficiency, fine texture detail reconstruction, and easy textured mesh extraction.
- Score: 37.33701393691611
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Gaussian splatting techniques have shown promising results in novel view synthesis, achieving high fidelity and efficiency. However, their high reconstruction quality comes at the cost of requiring a large number of primitives. We identify this issue as stemming from the entanglement of geometry and appearance in Gaussian Splatting. To address this, we introduce a neural shell texture, a global representation that encodes texture information around the surface. We use Gaussian primitives as both a geometric representation and texture field samplers, efficiently splatting texture features into image space. Our evaluation demonstrates that this disentanglement enables high parameter efficiency, fine texture detail reconstruction, and easy textured mesh extraction, all while using significantly fewer primitives.
Related papers
- Using Gaussian Splats to Create High-Fidelity Facial Geometry and Texture [2.7431069096660736]
We leverage increasingly popular three-dimensional neural representations in order to construct a unified explanation of a collection of uncalibrated images of the human face.<n>We leverage segmentation segmentation to facilitate the reconstruction of a neutral pose from only 11 images.<n>We show how accurate geometry enables the Gaussian Splats to be transformed into texture space where they can be treated as a view-dependent neural texture.
arXiv Detail & Related papers (2025-12-18T10:53:51Z) - ASAP-Textured Gaussians: Enhancing Textured Gaussians with Adaptive Sampling and Anisotropic Parameterization [51.51724817131134]
3D Gaussian Splatting has texture parameterizations to capture spatially varying attributes.<n>Textures are typically defined in canonical space, leading to inefficient sampling.<n>Our proposed ASAP Textured Gaussians significantly improve the quality efficiency tradeoff, achieving high-fidelity rendering with far fewer texture parameters.
arXiv Detail & Related papers (2025-12-16T03:13:27Z) - Content-Aware Texturing for Gaussian Splatting [4.861240703958262]
We propose to use texture to represent detailed appearance where possible.<n>Our main focus is to incorporate per-primitive texture maps that adapt to the scene during Gaussian Splatting optimization.<n>We show that our approach performs favorably in image quality and total number of parameters used compared to alternative solutions.
arXiv Detail & Related papers (2025-12-02T10:29:10Z) - Neural Texture Splatting: Expressive 3D Gaussian Splatting for View Synthesis, Geometry, and Dynamic Reconstruction [20.80508604651125]
3D Gaussian Splatting (3DGS) has emerged as a leading approach for high-quality novel view synthesis.<n>Recent works have proposed to augment 3DGS with additional per-primitive capacity, such as per-splat textures.<n>We introduce Neural Texture Splatting (NTS) to improve state-of-the-art 3DGS variants across a wide range of reconstruction tasks.
arXiv Detail & Related papers (2025-11-24T08:26:32Z) - A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction [61.205927223522174]
We propose a novel framework that enables Gaussian Splatting to incorporate multiple types of primitives during its surface reconstruction process.<n>Specifically, in our framework, we first propose a compositional splatting strategy, enabling the splatting and rendering of different types of primitives.
arXiv Detail & Related papers (2025-07-15T13:52:40Z) - TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting [6.430258446597413]
Gaussian Splatting have demonstrated remarkable novel view synthesis performance at high rendering frame rates.<n>We propose a method that tackles this issue through a geometrically and physically grounded radiance field.<n>We also propose to harness the GPU hardware to accelerate rendering at test time via unified material texture atlas.
arXiv Detail & Related papers (2025-06-16T10:41:40Z) - SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction [48.228533595941556]
We propose a novel method called SolidGS to address this problem.<n>We observed that the reconstructed geometry can be severely inconsistent across multi-views.<n>With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction.
arXiv Detail & Related papers (2024-12-19T21:04:43Z) - NeRF-Texture: Synthesizing Neural Radiance Field Textures [77.24205024987414]
We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images.<n>In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape.<n>We can synthesize NeRF-based textures through patch matching of latent features.
arXiv Detail & Related papers (2024-12-13T09:41:48Z) - Textured Gaussians for Enhanced 3D Scene Appearance Modeling [58.134905268540436]
3D Gaussian Splatting (3DGS) has emerged as a state-of-the-art 3D reconstruction and rendering technique.<n>We propose a new generalized Gaussian appearance representation that augments each Gaussian with alpha(A), RGB, or RGBA texture maps.<n>We demonstrate image quality improvements over existing methods while using a similar or lower number of Gaussians.
arXiv Detail & Related papers (2024-11-27T18:59:59Z) - GStex: Per-Primitive Texturing of 2D Gaussian Splatting for Decoupled Appearance and Geometry Modeling [11.91812502521729]
Gaussian splatting has demonstrated excellent performance for view synthesis and scene reconstruction.<n>Since each Gaussian primitive encodes both appearance and geometry, appearance modeling requires a number of Gaussian primitives.<n>We propose to employ perprimitive representation so that even a single Gaussian can be used to capture appearance details.
arXiv Detail & Related papers (2024-09-19T17:58:44Z) - Textured-GS: Gaussian Splatting with Spatially Defined Color and Opacity [7.861993966048637]
We introduce Textured-GS, an innovative method for rendering Gaussian splatting using Spherical Harmonics (SH)
This approach enables each Gaussian to exhibit a richer representation by accommodating varying colors and opacities across its surface.
Our experiments show that Textured-GS consistently outperforms both the baseline Mini-Splatting and standard 3DGS in terms of visual fidelity.
arXiv Detail & Related papers (2024-07-13T00:45:37Z) - Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering [47.78392889256976]
Paint-it is a text-driven high-fidelity texture map synthesis method for 3D rendering.
Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS)
We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS.
arXiv Detail & Related papers (2023-12-18T17:17:08Z) - NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support [43.5015470997138]
We present a method for generating high-quality watertight manifold meshes from multi-view input images.<n>Our method combines the benefits of both worlds; we take the geometry obtained from neural fields, and further optimize the geometry as well as a compact neural texture representation.
arXiv Detail & Related papers (2023-05-26T17:59:21Z) - Pyramid Texture Filtering [86.15126028139736]
We present a simple but effective technique to smooth out textures while preserving the prominent structures.
Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures.
We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts.
arXiv Detail & Related papers (2023-05-11T02:05:30Z)
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.