The paper introduces a compact neural appearance model for 3D Gaussian Splatting that replaces traditional low‑order spherical harmonics (SH) with a tiny shared MLP decoding per‑primitive latent codes. It compares SH with recent spherical appearance models, integrating all into a unified CUDA rasterizer and WebGL viewer, and demonstrates that the new neural representation reduces per‑primitive appearance storage from 192 to 28 bytes, speeds optimization by 1.3×, and improves reconstruction quality. The study also analyzes how different appearance parametrizations affect geometry recovery and the handling of non‑static scene content.
Recent advances in 3D Gaussian Splatting have demonstrated unprecedented success in novel view synthesis. However, the substantial inference and storage overhead driven by high-order Spherical Harmonics (SH) are primary bottlenecks for mobile platforms.
arXiv:2608.22344v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from "model bloat" - a large number of redunda...
ReSplat introduces a recurrent Gaussian splatting model that iteratively refines 3D Gaussians using the rendering error as a feedback signal, avoiding explicit gradient computation. The method starts from a compact reconstruction in a subsampled space, producing far fewer Gaussians than prior per‑pixel models, which reduces computational cost. Experiments on multiple datasets, view counts, and resolutions show state‑of‑the‑art performance with faster rendering speeds.
arXiv:2609. 03334v1 Announce Type: new Abstract: A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which increases optimization cost and slows convergence, especially at high resolutions.
CC-4DGS introduces a storage‑efficient framework for dynamic 4D Gaussian splatting by replacing large multi‑resolution hash tables with a deterministic dense hash encoding and compact neural decoders, reducing deformation storage to 1–3 MB per scene. It also compresses canonical point‑cloud attributes through conditional autoencoding, selective quantization, and residual codebooks, achieving 3–5× reduction in point‑cloud size with negligible quality loss. The combined approach maintains real‑time rendering performance while lowering total storage to 20–30 MB, matching state‑of‑the‑art reconstruction accuracy on N3DV and Technicolor Light Field datasets.
KISS-GS is a modular compression pipeline for 3D Gaussian Splatting (3DGS) scenes that separates compression from training. It first compacts a vanilla 3DGS scene by 15.7× using state‑of‑the‑art pruning, then encodes the result into the SOG‑XT image‑based format, achieving an additional 6.6× reduction. Optional encoding‑aware fine‑tuning can further cut the size by 2.2×, yielding total reductions of 85× to 319× on standard benchmarks while enabling web‑native decoding.
arXiv:2606. 02068v1 Announce Type: cross Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) delivering impressive results.
arXiv:2603.16103v3 Announce Type: replace Abstract: 3D Gaussian Splat (3DGS) enables high-fidelity, real-time novel view synthesis by representing scenes with large sets of anisotropic primitives, bu...
arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.
WilLaGS introduces a unified framework that enhances 3D Gaussian Splatting for in-the-wild scenes by learning a continuous global appearance manifold with a β‑VAE and generating dynamic Tri‑Plane features for spatially‑varying local illumination. It also incorporates a self‑supervised perceptual masking mechanism using a Teacher‑Student EMA architecture to suppress transient artifacts and identify inconsistent regions. Experiments on multiple datasets show that WilLaGS achieves state‑of‑the‑art reconstruction quality and novel view synthesis while preserving real‑time rendering efficiency.
InceptionGS is a method that improves large‑scale Gaussian splatting for scenes captured with unstructured view sampling. It starts from an initial Gaussian splatting and selectively repairs areas affected by sparse views by integrating scene‑ and view‑adaptive generative priors, while keeping well‑covered regions unchanged. Experiments on real‑world scenes show that this hybrid reconstruction‑generation approach yields higher‑fidelity results than existing methods.
arXiv:2609.01516v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor...