arXiv:2604. 01204v3 Announce Type: replace-cross Abstract: Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstruction tasks.
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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.
We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support.
arXiv:2609.23380v1 Announce Type: new Abstract: Gaussian Splatting has enabled real-time novel view synthesis, but its tightly coupled geometry and appearance representation often require a large num...
arXiv:2605.26616v2 Announce Type: replace Abstract: While 3D Gaussian Splatting has achieved remarkable success in photorealistic novel view synthesis, its pursuit of fast and high-fidelity 3D recons...
arXiv:2609.37115v1 Announce Type: new Abstract: We revisit the role of appearance modeling in 3D Gaussian Splatting (3DGS) and show that limited expressiveness in view-dependent reflectance is a key...
arXiv:2604.04925v3 Announce Type: replace Abstract: Generating procedural synthetic data for multi-view stereo (MVS) usually requires writing complex rules to match the realism of curated datasets. W...