We present a fast 3DGS reconstruction pipeline designed to converge within one minute, developed for the SIGGRAPH Asia 3DGS Fast Reconstruction Challenge. The challenge consists of an initial round using SLAM-generated camera poses (with noisy trajectories) and a final round using COLMAP poses (highly accurate). To robustly handle these heterogeneous settings, we develop a two-stage solution. In the first round, we use reverse per-Gaussian parallel optimization and compact forward splatting based on Taming-GS and Speedy-splat, load-balanced tiling, an anchor-based Neural-Gaussian representation enabling rapid convergence with fewer learnable parameters, initialization from monocular depth and partially from feed-forward 3DGS models, and a global pose refinement module for noisy SLAM trajectories. In the final round, the accurate COLMAP poses change the optimization landscape; we disable pose refinement, revert from Neural-Gaussians back to standard 3DGS to eliminate MLP inference overhead, introduce multi-view consistency-guided Gaussian splitting inspired by Fast-GS, and introduce a depth estimator to supervise the rendered depth. Together, these techniques enable high-fidelity reconstruction under a strict one-minute budget. Our method achieved the top performance with a PSNR of 28.43 and ranked first in the competition.

翻译：我们提出了一种快速三维高斯溅射重建流程，旨在实现一分钟内的收敛，专为SIGGRAPH Asia三维高斯溅射快速重建挑战赛而开发。该挑战赛包含使用SLAM生成相机姿态（含噪声轨迹）的初赛轮次，以及使用高精度COLMAP姿态的决赛轮次。为稳健处理这些异构设置，我们开发了一种两阶段解决方案。在初赛轮次中，我们采用基于Taming-GS与Speedy-splat的反向逐高斯并行优化与紧凑前向溅射、负载均衡分块处理、基于锚点的神经-高斯表示（以更少可学习参数实现快速收敛）、从单目深度及部分前馈三维高斯溅射模型初始化，以及针对含噪声SLAM轨迹的全局姿态优化模块。在决赛轮次中，精确的COLMAP姿态改变了优化格局；我们停用姿态优化，将神经-高斯表示恢复为标准三维高斯溅射以消除MLP推理开销，引入受Fast-GS启发的多视图一致性引导高斯分裂策略，并采用深度估计器监督渲染深度。这些技术共同实现了严格一分钟时限下的高保真重建。我们的方法以28.43的峰值信噪比获得最佳性能，在竞赛中位列第一。