We present a novel-view rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome the limitations of existing 3D Gaussian splatting algorithms. Prior neural rendering methods suffer from severe splat drift due to scene complexity and insufficient multi-view observation, and can fail to fix splats on the true geometry in ground-robot datasets. Our method integrates pixel-aligned anchors from monocular depths and generates Gaussian splats around these anchors using residual-form Gaussian decoders. To address the inherent scale ambiguity of monocular depth, we parameterize anchors with per-view depth-scales and employ scale-consistent depth loss for online scale calibration. Our method results in improved rendering performance, based on PSNR, SSIM, and LPIPS metrics, in ground scenes with free trajectory patterns, and achieves state-of-the-art rendering performance on the R3LIVE odometry dataset and the Tanks and Temples dataset.

翻译：本文提出一种新颖的视角渲染算法Mode-GS，专为地面机器人轨迹数据集设计。该方法基于锚定高斯溅射技术，旨在克服现有三维高斯溅射算法的局限性。先前基于神经辐射场的方法因场景复杂性和多视角观测不足而存在严重的溅射漂移问题，难以在地面机器人数据集的真实几何结构上稳定固定溅射点。本方法通过整合单目深度估计生成的像素对齐锚点，并利用残差形式的高斯解码器在这些锚点周围生成高斯溅射。为应对单目深度固有的尺度模糊性，我们采用逐视角深度尺度参数化锚点，并利用尺度一致性深度损失进行在线尺度校准。实验表明，在自由轨迹模式的地面场景中，本方法在PSNR、SSIM和LPIPS指标上均取得显著提升的渲染性能，并在R3LIVE里程计数据集与Tanks and Temples数据集上实现了最先进的渲染效果。