Micro-splatting: Multistage Isotropy-informed Covariance Regularization Optimization for High-Fidelity 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2504.05740v2
• Date: Tue, 02 Sep 2025 10:05:44 GMT
• Title: Micro-splatting: Multistage Isotropy-informed Covariance Regularization Optimization for High-Fidelity 3D Gaussian Splatting
• Authors: Jee Won Lee, Hansol Lim, Sooyeun Yang, Jongseong Brad Choi,
• Abstract summary: Micro-Splatting is a unified, in-training pipeline that preserves visual detail while drastically reducing model complexity.<n>On four object-centric benchmarks, Micro-Splatting reduces splat count and model size by up to 60% and shortens training by 20%.<n>Results demonstrate that Micro-Splatting delivers both compactness and high fidelity in a single, efficient, end-to-end framework.
• Score: 1.5582756275568836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-fidelity 3D Gaussian Splatting methods excel at capturing fine textures but often overlook model compactness, resulting in massive splat counts, bloated memory, long training, and complex post-processing. We present Micro-Splatting: Two-Stage Adaptive Growth and Refinement, a unified, in-training pipeline that preserves visual detail while drastically reducing model complexity without any post-processing or auxiliary neural modules. In Stage I (Growth), we introduce a trace-based covariance regularization to maintain near-isotropic Gaussians, mitigating low-pass filtering in high-frequency regions and improving spherical-harmonic color fitting. We then apply gradient-guided adaptive densification that subdivides splats only in visually complex regions, leaving smooth areas sparse. In Stage II (Refinement), we prune low-impact splats using a simple opacity-scale importance score and merge redundant neighbors via lightweight spatial and feature thresholds, producing a lean yet detail-rich model. On four object-centric benchmarks, Micro-Splatting reduces splat count and model size by up to 60% and shortens training by 20%, while matching or surpassing state-of-the-art PSNR, SSIM, and LPIPS in real-time rendering. These results demonstrate that Micro-Splatting delivers both compactness and high fidelity in a single, efficient, end-to-end framework.

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