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.
By Florian Hahlbohm, Jorge Condor, Linus Franke, Martin Eisemann, Marcus Magnor
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...
By Butian Xiong, Rong Liu, Tiantian Zhou, Meida Chen, Zhiwen Fan, Andrew Feng
arXiv:2608.29106v1 Announce Type: new
Abstract: While neural rendering methods such as 3D Gaussian Splatting achieve remarkable visual fidelity, traditional polygonal meshes remain the backbone of es...
By Tian Shi, Shenhan Qian, Daniel Cremers
arXiv:2608.20687v1 Announce Type: new
Abstract: 3D Gaussian Splatting has achieved remarkable success in novel view synthesis. However, extracting high-fidelity surfaces directly from 3DGS remains ch...
By Chuanjin Fan, Wenjie Chang, Bohao Liao, Yujia Chen, Wenfei Yang, Tianzhu Zhang
arXiv:2606. 05124v1 Announce Type: cross Abstract: After the success of 3D Gaussian Splatting (3DGS) for novel view synthesis, many works have explored how to also use it for geometric surface representation.
By Hongyu Zhou, Zorah L\"ahner
MEGA is a new framework that extracts object-level, watertight meshes from 3D Gaussian Splatting (3DGS) scenes. It uses a segment-then-mesh approach, leveraging Spatial Visual Distillation (SVD) to sample diverse camera views of each segmented object and train a mesh reconstruction model with photometric supervision. Experiments on popular benchmarks show that MEGA outperforms existing methods in accurately recovering object-level 3D occupancy and supports complex physical interactions by combining high-quality meshes with photorealistic 3DGS rendering.
By Liwei Liao, Yingkui Zhang, Qianqian Tong, Ronggang Wang
3D Gaussian Splatting has achieved remarkable success in photorealistic and efficient rendering, leading to a rapid increase in 3D assets represented by 3D Gaussian primitives. Directly rigging these assets with arbitrary skeleton topologies is highly desirable.
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.
By Yixiong Yang, Sisheng Zhang, Qingsong Yan, Shaohuai Shi, Qiang Wang
arXiv:2606. 31637v1 Announce Type: cross Abstract: Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image editing as it enables the modification of object colors and textures without altering lighting.
By Alexandre Lanvin, Jeffrey Hu, Simon Lucas, Adrien Bousseau, George Drettakis
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...
By Zhiwei Li, Yijia Guo, Yishi Lu, Liwen Hu, Hong Rao, Shengbo Chen, Lei Ma
JanusMesh introduces a fast, training‑free framework for creating 3D visual illusion meshes that reveal different semantics from various viewpoints. The method splits generation into two stages: a cross‑space dual‑branch denoising process that aligns 3D latents with CLIP guidance and blends Signed Distance Fields for seamless geometry, followed by a view‑conditioned texture synthesis module that aggregates 2D diffusion priors onto the fused mesh. Experiments show that JanusMesh produces highly realistic, dual‑semantic 3D illustrations in only 3–5 minutes, outperforming prior approaches in geometric integrity, semantic recognizability, and efficiency.
By Siang-Ling Zhang, Huai-Hsun Cheng, Tsung-Ju Yang, Yu-Lun Liu
arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.
By Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben, Steve Petruzza, Qi Wu, Will Usher, Sidharth Kumar