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

Title: 微点绘制：用于高保真3D高斯点绘制的多阶段各向同性信息协方差正则化优化

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. Show Chinese abstract

Abstract: 高保真3D高斯点云方法在捕捉精细纹理方面表现出色，但往往忽视模型的紧凑性，导致大量的点云数量、内存膨胀、训练时间长和复杂的后期处理。 我们提出了Micro-Splatting：两阶段自适应增长与优化，这是一种统一的训练中流程，在不进行任何后期处理或辅助神经模块的情况下，保留视觉细节的同时显著降低模型复杂度。 在第一阶段（增长）中，我们引入基于轨迹的协方差正则化，以保持近各向同性的高斯分布，减轻高频区域的低通滤波，并改善球面谐波颜色拟合。 然后我们应用基于梯度引导的自适应密集化，仅在视觉复杂区域细分点云，使平滑区域保持稀疏。 在第二阶段（优化）中，我们使用简单的透明度尺度重要性评分来修剪低影响点云，并通过轻量级的空间和特征阈值合并冗余邻居，生成一个精简但细节丰富的模型。 在四个以物体为中心的基准测试中，Micro-Splatting将点云数量和模型大小减少了高达60%，并将训练时间缩短了20%，同时在实时渲染中达到或超过了最先进的PSNR、SSIM和LPIPS指标。 这些结果表明，Micro-Splatting在一个高效、端到端的框架中实现了紧凑性和高保真度。