DepthGS introduces a depth-guided Gaussian splatting framework that leverages depth discrepancies to bound reconstruction error in both single-view and multi-view settings. The method proposes adaptive normal regularization for single-view optimization and cross-view depth consistency regularization for multi-view optimization, guided by theoretical depth-error relationships. Experiments on DTU, Mip-NeRF360, and Tanks and Temples show that DepthGS achieves competitive performance in surface reconstruction and novel view synthesis.
arXiv:2609.36969v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is a state-of-the-art technique for 3D scene rendering, offering high efficiency and excellent visual quality. However, be...
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.
arXiv:2609.22941v1 Announce Type: new Abstract: Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and m...
Sparse-GS2Mesh is a stereo-aware framework that improves 3D surface reconstruction from sparse camera views by combining 3D Gaussian Splatting (3DGS) with stereo matching techniques. The method begins by initializing 3DGS with epipolar depth priors to reduce overfitting, then applies adaptive baseline selection, stereo-matching‑guided fine‑tuning, and 2D/3D co‑regularization to refine the model. Experiments show a 15% performance gain over state‑of‑the‑art methods in low‑overlap scenarios and comparable results in high‑overlap settings.
The paper introduces AffineGS, a method that improves the geometric accuracy of 3D Gaussian Splatting (3DGS) by applying a spatially varying affine rectification to monocular depth estimates. It identifies that monocular depth networks suffer from region-dependent, piecewise-linear distortions, rendering global alignment ineffective. By employing edge-aware, regionally smooth fields and a normal-aware regularizer, AffineGS aligns disparity with 3DGS depth and normals, achieving strong photometric quality while significantly enhancing geometry across diverse experiments.
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: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:2605.10360v3 Announce Type: replace Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains...
arXiv:2511.16030v3 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervi...
OctreeSplat introduces a hierarchical octree-guided feed‑forward Gaussian splatting method that extracts multi‑view features and geometric priors from a frozen visual foundation model. By constructing a hierarchical octree and predicting densification scores, it allocates Gaussian primitives at spatially varying densities, improving depth rendering and surface reconstruction. The approach requires only 76 million additional parameters and 35 GPU‑hours to train, and demonstrates competitive RGB view synthesis, depth synthesis, and surface reconstruction on Replica, TUM, Tanks and Temples, and DTU datasets while reducing the number of Gaussian primitives needed.
Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time rendering. However, existing methods typically require dense multi-view videos for sufficient geometric constraints, making capture expensive and limiting sparse-camera deployment.
The paper introduces Wide-View Gaussian Splatting (WV‑GS), a feed‑forward framework that synthesizes geometrically consistent wide views from multiple perspective cameras in autonomous driving. WV‑GS addresses challenges such as peripheral stretching and photometric inconsistencies by using Piecewise Perspective Rendering and Spatial Affine Photometric Modeling, eliminating the need for per‑scene optimization. The authors also present WideDrive, a synthetic benchmark with dense ground‑truth wide views, and adapt a LiDAR‑projected sparse‑reference protocol for real‑world datasets, demonstrating the method’s effectiveness across multiple datasets.