Texture3dgs: GPU-Optimized 3D Gaussian Splatting

• Texture3dgs is a high-fidelity 3D modeling technique that uses Gaussian primitives and structured texture mappings for efficient scene synthesis.
• It leverages representations like triplane and UV atlas to enable generative modeling, extreme compression, and photorealistic reconstruction.
• It features hardware-aware algorithms optimized for mobile GPUs, achieving significant speedups, memory reductions, and enhanced rendering performance.

Texture3dgs refers both generically to high-fidelity, attribute-rich 3D Gaussian Splatting (3DGS) models for scene synthesis, deformation, and generative tasks, and specifically to a mobile GPU-optimized 3DGS pipeline featuring advanced texture cache–aware algorithms. The term encompasses representations, compression strategies, generative frameworks, deformation techniques, and hardware-specific algorithms that address geometry-texture joint modeling, efficient rasterization, and practical deployment for photorealistic 3D reconstruction and synthesis.

1. Mathematical Foundations of 3D Gaussian Splatting and Texture Encoding

A 3DGS model consists of $N$ Gaussian primitives: $g_i = \{\mu_i, \Sigma_i, c_i, \alpha_i\}$, where $\mu_i \in \mathbb{R}^3$ is the center, $\Sigma_i \in \mathbb{R}^{3 \times 3}$ is the covariance (defining size, orientation, anisotropy), $c_i$ is a color field (often view-dependent, typically encoded via spherical harmonics), and $\alpha_i$ is opacity. The volumetric contribution to density at position $x$ is

$$\phi_i(x) = (2\pi)^{-3/2} |\Sigma_i|^{-1/2} \exp\left[-\frac{1}{2}(x - \mu_i)^\top \Sigma_i^{-1} (x - \mu_i)\right] \cdot \alpha_i$$

and the radiance along view $v$ is $c_i(v)$. Texture fidelity arises from the ability to preserve, compress, manipulate, and render $c_i(v)$ and the spatial relationships of the Gaussians, enabling sharp, multiview-consistent appearance.

2. Structured Representations for Textured 3DGS: Triplanes and UV Mapping

Efficient texture encoding in 3DGS exploits structured mappings. DirectTriGS (Ju et al., 10 Mar 2025) introduces a triplane representation: three orthogonal 2D grids (XY, XZ, YZ), with geometry and appearance attributes split among channels. For any location $p$, attribute vectors are sampled via bilinear interpolation from each plane and concatenated:

• $F_\text{geo}(p)$ encodes surface shape;
• $F_\text{app}(p)$ encodes texture and GS attributes (scale, color, opacity).

UVGS (Rai et al., 3 Feb 2025) encodes 3D Gaussians into a 2D UV atlas by spherical mapping:

• Each $g_i$ is mapped to a pixel in an $M \times N$ image, with up to $K$ layers for complex scenes.
• Each pixel/texel contains a 14-channel vector: position (3), rotation (4), scale (3), opacity (1), color (3).
• To facilitate standard 2D deep models, a multi-branch CNN compresses these 14 channels into 3 "Super" channels (Super UVGS), invertible back to the full representation via a mirrored network.

Both triplane and UV mappings enable use of VAEs, latent diffusion, and 2D perceptual losses for end-to-end joint geometry and texture synthesis, compression (99%+), and inpainting.

3. Generative and Compression Frameworks for Texture3dgs

Generative modeling is achieved through multi-stage pipelines leveraging structured encodings and deep learning:

• DirectTriGS: Uses a differentiable TriRenderer to decode triplane fields into surface samples and appearance vectors. It supervises rendering with multi-view 2D losses (e.g., LPIPS, SSIM) and compresses triplane tensors via VAE. Text-to-3D synthesis proceeds through staged latent diffusion: geometry code $z_g$ is generated from text prompt $t$, appearance code $z_a$ from $t$ and $z_g$, and decoded back to triplanes then to GS splats (Ju et al., 10 Mar 2025).
• UVGS: Super UVGS images are encoded as VAE latents and synthesized using diffusion models. Generation, compression, reconstruction, and inpainting leverage the direct invertibility of the UVGS image to the original Gaussian set (Rai et al., 3 Feb 2025).
• Hyper-3DG: Employs hypergraph learning to refine textures and geometry, building high-order correlations among explicit and latent attributes in 3DGS clusters. ISM loss ensures fidelity across diffusion intervals, while the HGRefiner enforces multiview, spatial, and appearance consistency via hypergraph convolution (Di et al., 14 Mar 2024).

Quantitative scores reflect the texture fidelity: DirectTriGS achieves CLIP(0.2462), UVGS yields PSNR(31.2 dB) for round-trip compression, Hyper-3DG user paper average of 4.1/5 for synthesized texture.

4. Deformation and Texture Preservation Mechanisms

Deforming textured 3DGS while maintaining appearance requires careful covariance propagation. CAGE-GS (Tong et al., 17 Apr 2025) introduces:

• Cage-based deformation: The source GS scene is aligned to a target shape using a learned cage, with mean-value coordinates guiding the movement of each Gaussian center.
• Jacobian-driven covariance update: For each Gaussian, the local deformation $\Phi$ is linearized at its center via Jacobian $J$, with the new covariance

$\Sigma' = J \Sigma J^T$

where $\Sigma = R S^2 R^T$ (with $R$ rotation, $S$ scale). Orientation and scale are extracted by SVD. Texture fidelity is preserved as the anisotropic spread and orientation are correctly transported, avoiding blur/streak artifacts.

• Sampling + kNN acceleration: Only a subset of Gaussians are sampled for full Jacobian computation, with their neighbors assigned the same linearization, reducing update time from 169.7 min (full) to 7.3 min for a chair scene.

Evaluation via Chamfer Distance, DINO score, and user studies confirms that updating both center and covariance maintains sharpness and detail, outperforming methods that move only $\mu$.

5. Hardware-Conscious Implementations: Texture3dgs for Mobile GPU

Texture3dgs (Sanim et al., 20 Nov 2025) refers to a pipeline that adapts 3DGS rendering for mobile GPUs by optimizing for the limited, spatially-local 2D texture cache:

• Four stages: Preprocessing, tile-based duplication, layout-transform bitonic sorting, and tilewise alpha-composite rendering.
• Sorting algorithm: Index/layout-transformation bitonic sort places comparator pairs in adjacent texel locations, minimizing cross-block cache misses. The sort key comprises tile index and quantized depth, packed into a single float.
• Cache cost model: Latency per fetch is modeled as $$C_\text{miss}(\Delta_x, \Delta_y) = \alpha N_\text{miss} + \beta D_\text{stride}$$, allowing empirical guidance of variable layout.
• Texture packing: Gaussian parameters are grouped into RGBA texels to maximize locality and minimize memory.
• Performance: Achieves up to 4.1× sorting speedup, 1.7× pipeline speedup, and 1.6× memory reduction versus baselines; L1 cache misses decrease by 60% (Sanim et al., 20 Nov 2025).

General deployment recommendations include profiling stride latency per SoC, enforcing square, power-of-2 texture dimensions, and leveraging block-wise data layout for rendering.

6. Challenges, Solutions, and Reported Results

Standard challenges in textured 3DGS include permutation invariance, loss of texture fidelity under deformation, and resource inefficiency on mobile platforms. Recent methods address:

• Texture compression and scalability: UVGS/DirectTriGS allow scaling atlas resolution and layer count to support millions of Gaussians, with minimal loss in perceptual accuracy. Higher UV resolution increases effective texture and geometry capacity.
• Multiview-consistency and high-frequency detail: Hyper-3DG addresses over-smoothness and the Janus problem via hypergraph-enforced high-order correlations, integrating spatial and latent image features.
• Mobile efficiency trade-offs: Texture3dgs on mobile platforms shows dominant sorting bottlenecks; bitonic layout transforms yield the largest single-stage improvements, with remaining gains from texture packing and block layouts.

Comparative results are summarized below:

Method	CD	DINO	User/Fidelity
CAGE-GS	0.0997	0.402	63.3% (user votes)
DirectTriGS	—	—	CLIP=0.2462, 3.38/5
UVGS	—	—	PSNR=31.2 dB (AE)
Hyper-3DG	—	—	4.1/5 (user paper)
Texture3dgs	—	—	4.1× (sort speedup)

This suggests that both structured generative models and hardware-aware pipelines are converging to support sharp, efficient, and photorealistic textured 3DGS synthesis and usage across platforms.

7. Future Directions and Recommendations

Potential extensions for Texture3dgs encompass adaptive-resolution spatial layouts (quadtree, HEALPix tilings), generalization of cache-aware algorithms to mobile NPUs/DSPs, and further joint optimization of geometry-texture representations for tasks such as text-based 3D creation, style transfer, and multiview inpainting. Portability across SoCs has already been demonstrated in compute APIs such as OpenCL and Vulkan (Sanim et al., 20 Nov 2025), while multibranch attribute compression (UVGS) and hypergraph refinement (Hyper-3DG) hold promise for increasing generative diversity and realism.

A plausible implication is that future textured 3DGS models will bridge lattice-based 2D generative learning, physical scene deformation, and ultra-efficient device-side rendering, forming the substrate for next-generation interactive, personalized and resource-aware 3D synthesis applications.