arXiv Computer Vision
NRF-GS: Neural Residual Fields for Expressive and Compact Gaussian Splatting
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 7
Compact Neural Appearance Models for Efficient Gaussian Splatting
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.
Hugging Face Trending Papers
Jun 29
Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting
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 Computer Vision
Sep 1
BRF-GS: Hyperspectral Bidirectional Reflectance Factor Modeling and Image Generation Based on 3D Gaussian Splatting
arXiv:2608.31159v1 Announce Type: new Abstract: The bidirectional reflectance factor (BRF) characterizes the directional radiative properties of terrestrial surfaces. However, existing three-dimensio...
arXiv Computer Vision
2d ago
AESplat: Advancing Pose-Free Feed-Forward 3D Gaussian Splatting via Decoupled Appearance Modeling
AESplat is a new pose‑free feed‑forward 3D Gaussian Splatting framework that improves rendering quality by decoupling view‑independent and view‑dependent appearance modeling. It directly extracts the base view‑independent appearance from input images and predicts higher‑order spherical harmonic coefficients with a shallow MLP that incorporates 3D‑aware inductive biases. Experiments on several datasets show AESplat outperforms state‑of‑the‑art methods, achieving up to 0.8 dB higher PSNR than NAS3R and 1.1 dB over DepthSplat on RealEstate10K.
arXiv AI
Jun 3
Ref-DGS: Reflective Dual Gaussian Splatting
arXiv:2603. 07664v3 Announce Type: replace-cross Abstract: The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthesis.
arXiv Computer Vision
Sep 14
NOVA-GS: Noise-Aware View-Consistent Gaussian Splatting for Low-Light Novel View Synthesis
arXiv:2609.12682v1 Announce Type: new Abstract: Reconstructing 3D scenes under real-world low-light conditions remains challenging due to severe sensor noise, low signal-to-noise ratios, and degraded...