SplatCo: Structure-View Collaborative Gaussian Splatting for Detail-Preserving Rendering of Large-Scale Unbounded Scenes
This addresses the challenge of rendering complex outdoor environments with fine details for applications like aerial capture and 3D reconstruction, representing a strong specific gain rather than an incremental improvement.
The paper tackles the problem of high-fidelity rendering of large-scale unbounded scenes by proposing SplatCo, a structure-view collaborative Gaussian splatting framework, achieving PSNR improvements of 1-2 dB and SSIM gains of 0.1 to 0.2 over state-of-the-art methods.
We present SplatCo, a structure-view collaborative Gaussian splatting framework for high-fidelity rendering of complex outdoor environments. SplatCo builds upon two novel components: (1) a cross-structure collaboration module that combines global tri-plane representations, which capture coarse scene layouts, with local context grid features that represent fine surface details. This fusion is achieved through a novel hierarchical compensation strategy, ensuring both global consistency and local detail preservation; and (2) a cross-view assisted training strategy that enhances multi-view consistency by synchronizing gradient updates across viewpoints, applying visibility-aware densification, and pruning overfitted or inaccurate Gaussians based on structural consistency. Through joint optimization of structural representation and multi-view coherence, SplatCo effectively reconstructs fine-grained geometric structures and complex textures in large-scale scenes. Comprehensive evaluations on 13 diverse large-scale scenes, including Mill19, MatrixCity, Tanks & Temples, WHU, and custom aerial captures, demonstrate that SplatCo consistently achieves higher reconstruction quality than state-of-the-art methods, with PSNR improvements of 1-2 dB and SSIM gains of 0.1 to 0.2. These results establish a new benchmark for high-fidelity rendering of large-scale unbounded scenes. Code and additional information are available at https://github.com/SCUT-BIP-Lab/SplatCo.