The paper introduces Reflection-aware Gaussian Splatting (RGS), a physically-based deferred rendering framework that improves novel view synthesis for reflective objects. RGS leverages a powerful 3D foundation model to provide a strong geometric prior and employs cross-view shape consistency regularization to prevent surface collapse and reduce geometric hollows. Additionally, a reflection-aware densification strategy captures specular variations across views, resulting in higher-quality renderings of reflective objects.
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: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.
Reflections from shiny objects and glass facades naturally extend the field of view of a camera, capturing the surrounding environment without the need to pan the camera or acquire a full panorama. We propose PanoLess, a Gaussian-splat-based framework that reconstructs the surrounding environment as a distant illumination map from images captured on only one side of a reflective surface.
arXiv:2606. 29379v1 Announce Type: cross Abstract: Gaussian splatting (GS) has garnered significant attention in VR/AR and digital content creation due to its explicit parameterization and efficient rendering capabilities.
arXiv:2312. 00206v4 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis.
arXiv:2608.22740v1 Announce Type: new Abstract: Generalizable 3D Gaussian Splatting (G-3DGS) has emerged as a promising approach for novel view synthesis undersparse-view settings. However, existing...
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...
3D Gaussian Splatting (3DGS) has recently enabled real-time novel view synthesis with impressive quality. However, it struggles to recover accurate surfaces under limited viewpoints and due to the inherent irregularity of Gaussian primitives.
arXiv:2607.20813v2 Announce Type: replace Abstract: Pixel-aligned Gaussian splatting enables efficient and generalizable novel-view synthesis. However, high-resolution rendering faces a critical trad...
GS-PI introduces an optimization‑decoupled framework that transforms Gaussian Splatting (GS) assets into physically based rendering (PBR) compatible Gaussian assets. By treating PBR material generation as a geometry‑conditioned diffusion process on 3D point clouds, it achieves multi‑view consistency and avoids the pixel‑correspondence problems of 2D diffusion. The method employs a multi‑scale cross‑view conditioning mechanism—combining global semantic priors, photometric cues, and spatial view‑direction signals—to prevent specular highlights from baking into intrinsic colors, and then distills the predicted attributes back into a fully relightable PBR‑GS asset without requiring proxy meshes.
CoMVS‑GS is a surface‑reconstruction framework that fuses Multi‑View Stereo (MVS) with 3D Gaussian splatting. It initializes Gaussian primitives from dense MVS points, uses PatchMatch‑3DGS mutual supervision to refine depths and normals, and replaces voxel‑based meshing with a Delaunay graph‑cut pipeline. Experiments on DTU, GauU‑Scene V2, and MatrixCity demonstrate competitive object‑level results and improved geometric accuracy and mesh compactness in outdoor scenes while preserving high rendering quality.
F4Splat introduces a feed‑forward predictive densification strategy for 3D Gaussian splatting that allocates Gaussians based on a densification‑score guided by spatial complexity and multi‑view overlap. The method predicts per‑region scores to estimate required Gaussian density, enabling explicit control over the total Gaussian budget without retraining. This adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, yielding compact yet high‑quality 3D representations and superior novel‑view synthesis performance with fewer Gaussians.