arXiv subjects

Mai Su

Publications and source records attributed to Mai Su.

5 recordsLinked to original sources

arXiv2026

BlitzGS: City-Scale Gaussian Splatting at Lightning Speed

Large-scale 3D Gaussian Splatting underpins digital twins, simulation, and aerial mapping, yet city-scale training remains computationally expensive even with multi-GPU execution because every iteration must preprocess, communicate, and rasterize an overly dense set of primitives. At any given step, only a small fraction of these primitives contribute meaningfully to the loss; the rest incur redundant storage, communication, and rasterization costs. Existing approaches improve individual cost factors but do not fully address the underlying question: which Gaussians should be stored on each GPU, rendered for each view, and retained after early geometry formation? We present BlitzGS, a distributed 3DGS framework that reduces the active Gaussian workload at three coupled levels. At the system level, it shards Gaussians across GPUs by index parity rather than spatial blocks, mitigating the cross-block visibility redundancy of spatial partitioning, and distributes each render step through a single cross-GPU exchange that routes projected Gaussians to their tile owners. At the model level, it controls the population from both ends of densification. Scheduled importance-scoring passes prune redundant survivors, and a lightweight spawn gate withholds candidates predicted not to survive. The same importance signal also feeds back into density control. At the view level, a distance-based LOD gate and an importance-based mask trim each camera's active set. On large-scale benchmarks, BlitzGS matches the rendering quality of recent large-scale baselines while delivering nearly an order-of-magnitude speedup, training city-scale scenes in tens of minutes. Our code is available at https://github.com/AkierRaee/BlitzGS.

cs.GR↗

arXiv2026

GVGS: Gaussian Visibility-Aware Multi-View Geometry for Accurate Surface Reconstruction

3D Gaussian Splatting (3DGS) enables efficient rendering, yet accurate surface reconstruction remains challenging due to unreliable geometric supervision. Existing approaches predominantly rely on depth-based reprojection to infer visibility and enforce multi-view consistency, leading to a fundamental circular dependency: visibility estimation requires accurate depth, while depth supervision itself is conditioned on visibility. In this work, we revisit multi-view geometric supervision from the perspective of visibility modeling. Instead of inferring visibility from pixel-wise depth consistency, we explicitly model visibility at the level of Gaussian primitives. We introduce a Gaussian visibility-aware multi-view geometric consistency (GVMV) formulation, which aggregates cross-view visibility of shared Gaussians to construct reliable supervision over co-visible regions. To further incorporate monocular priors, we propose a progressive quadtree-calibrated depth alignment (QDC) strategy that performs block-wise affine calibration under visibility-aware guidance, effectively mitigating scale ambiguity while preserving local geometric structures. Extensive experiments on DTU and Tanks and Temples demonstrate that our method consistently improves reconstruction accuracy over prior Gaussian-based approaches. Our code is fully open-sourced and available at an anonymous repository: https://github.com/GVGScode/GVGS.

cs.CV↗

arXiv2025

ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes

Multi-period image collections are common in real-world applications. Cities are re-scanned for mapping, construction sites are revisited for progress tracking, and natural regions are monitored for environmental change. Such data form multi-period scenes, where geometry and appearance evolve. Reconstructing such scenes is an important yet underexplored problem. Existing pipelines rely on incompatible assumptions: static and in-the-wild methods enforce a single geometry, while dynamic ones assume smooth motion, both failing under long-term, discontinuous changes. To solve this problem, we introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold. It's also designed to disentangle stable and evolving components, achieving temporally consistent reconstruction of multi-period scenes. To catalyze relevant research, we release ChronoScene dataset, a benchmark of real and synthetic multi-period scenes, capturing geometric and appearance variation. Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency. Our code and the ChronoScene dataset are publicly available at https://github.com/ZhongtaoWang/ChronoGS.

cs.GR↗

arXiv2025

HUG: Hierarchical Urban Gaussian Splatting with Block-Based Reconstruction for Large-Scale Aerial Scenes

3DGS is an emerging and increasingly popular technology in the field of novel view synthesis. Its highly realistic rendering quality and real-time rendering capabilities make it promising for various applications. However, when applied to large-scale aerial urban scenes, 3DGS methods suffer from issues such as excessive memory consumption, slow training times, prolonged partitioning processes, and significant degradation in rendering quality due to the increased data volume. To tackle these challenges, we introduce \textbf{HUG}, a novel approach that enhances data partitioning and reconstruction quality by leveraging a hierarchical neural Gaussian representation. We first propose a visibility-based data partitioning method that is simple yet highly efficient, significantly outperforming existing methods in speed. Then, we introduce a novel hierarchical weighted training approach, combined with other optimization strategies, to substantially improve reconstruction quality. Our method achieves state-of-the-art results on one synthetic dataset and four real-world datasets.

cs.GR↗

arXiv2023

CD$^2$: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance

Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression form obtained from deep learning models. However, the state-of-the-art schemes fail to directly generate well-structured meshes, and we identify that most meshes have severe Vertices Clustering (VC) and Illegal Twist (IT) problems. By analyzing the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is a root cause of VC and IT problems in deep learning model. In this paper, we initially demonstrate these two problems induced by CD loss with visual examples and quantitative analyses. Then, we propose a fine-grained reconstruction method CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive deformation. Extensive experiments on two 3D datasets and comparisons with five latest schemes demonstrate that our CD$^2$ directly generates a well-structured mesh and outperforms others in terms of several quantitative metrics.

cs.CV↗