arXiv AI

HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting

arXiv:2608. 14136v1 Announce Type: cross Abstract: Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptively capture scene content from coarse building structures to fine architectural details.

arXiv Computer Vision
Sep 4

STARS-GS: Structure-Aware Regularized Gaussian Splatting for Large-Scale Aerial Surface Reconstruction

STARS-GS is a new structure‑aware 3D Gaussian Splatting framework designed for large‑scale aerial surface reconstruction. It introduces a scene partitioning strategy that preserves continuous scene elements, a neighborhood‑aware Gaussian organization that extends geometric constraints to local neighborhoods, and an adaptive surface regularization that tailors regularization strength to local geometry. Experiments on aerial photogrammetry benchmarks show that STARS‑GS improves the average F1‑score from 0.640 to 0.698, a relative gain of about 9.1%.

By Bocheng Li, Wenjuan Zhang, Jie Pan. Dongxu Han, Xuesong Ma, Yiling Yao, Yaning Wang
arXiv Computer Vision
Sep 24

Fusion-Aware Direct 3D Gaussian Generation with Structured Patch Latent Flows

The paper introduces a fusion‑aware hierarchical Gaussian patch representation that enables direct class‑guided generation of 3D Gaussian Splatting (3DGS) objects. By decomposing irregular Gaussian sets into canonical local patches and encoding them as structured tokens, the method fuses global class semantics with patch‑level geometry, appearance, spatial correspondence, and rendering‑sensitive cues. A structure‑aware rectified flow model, conditioned on patch positions and coupled with global‑local velocity prediction and density‑aware weighting, produces class‑conditioned 3DGS objects within seconds, achieving more coherent geometry, sharper local details, and better multi‑view consistency than baseline models.

By Yizhao Wang, Jingbo Wang, Guantao Zhang
arXiv Computer Vision
Sep 4

Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery

Skyfall-GS is a hybrid framework that generates large‑scale, city‑block‑sized 3D urban scenes by combining satellite imagery for coarse geometry with open‑domain diffusion models for detailed appearance. It uses a curriculum‑driven iterative refinement to improve geometric completeness and photorealistic textures, eliminating the need for costly 3D annotations. Experiments show that Skyfall‑GS achieves better cross‑view geometry consistency and more realistic textures than existing methods.

By Jie-Ying Lee, Yi-Ruei Liu, Shr-Ruei Tsai, Wei-Cheng Chang, Chung-Ho Wu, Jiewen Chan, Zhenjun Zhao, Chieh Hubert Lin, Yu-Lun Liu
arXiv Computer Vision
Sep 3

PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting

PointGauss is a 3D-native framework that performs semantic parsing and instance segmentation on 3D Gaussian splatting representations by treating Gaussian primitives as unstructured point sets and extracting scale‑invariant geometric features with Point Transformer V3. It introduces an adaptive region‑of‑interest cropping strategy and an instance‑aware distance‑constrained rasterization pipeline to enable scalable, view‑consistent pixel‑level projections. The authors also release SplatSeg‑360, a cross‑scale benchmark with 32 complex scenes and over 6,300 aligned 2D‑3D masks, and show that PointGauss achieves real‑time performance with state‑of‑the‑art 3D‑mIoU (~90%) and 2D‑mIoU (~80%) scores.

By Wentao Sun, Yiping Chen, John S. Zelek, Jonathan Li