Recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated its potential for efficient and photorealistic 3D reconstructions, which is crucial for diverse applications such as robotics and immersive media. However, current Gaussian-based methods for dynamic scene reconstruction struggle with large inter-frame displacements, leading to artifacts and temporal inconsistencies under fast object motions. To address this, we introduce \textit{TrackerSplat}, a novel method that integrates advanced point tracking methods to enhance the robustness and scalability of 3DGS for dynamic scene reconstruction. TrackerSplat utilizes off-the-shelf point tracking models to extract pixel trajectories and triangulate per-view pixel trajectories onto 3D Gaussians to guide the relocation, rotation, and scaling of Gaussians before training. This strategy effectively handles large displacements between frames, dramatically reducing the fading and recoloring artifacts prevalent in prior methods. By accurately positioning Gaussians prior to gradient-based optimization, TrackerSplat overcomes the quality degradation associated with large frame gaps when processing multiple adjacent frames in parallel across multiple devices, thereby boosting reconstruction throughput while preserving rendering quality. Experiments on real-world datasets confirm the robustness of TrackerSplat in challenging scenarios with significant displacements, achieving superior throughput under parallel settings and maintaining visual quality compared to baselines. The code is available at https://github.com/yindaheng98/TrackerSplat.

翻译：三维高斯泼溅（3DGS）的最新进展已展示其在高效且逼真的三维重建中的潜力，这对机器人和沉浸式媒体等多样化应用至关重要。然而，当前基于高斯的动态场景重建方法在处理大帧间位移时存在困难，导致快速物体运动下出现伪影和时间不一致性。为解决此问题，我们提出TrackerSplat，一种新颖方法，融合先进点跟踪技术以增强3DGS在动态场景重建中的鲁棒性和可扩展性。TrackerSplat利用现成的点跟踪模型提取像素轨迹，并将每视图像素轨迹三角化映射至三维高斯，从而在训练前指导高斯的移动、旋转和缩放。该策略有效处理帧间大位移，显著减少先前方法中常见的淡化和重着色伪影。通过在基于梯度优化前精确定位高斯，TrackerSplat克服了多设备并行处理多个相邻帧时因大帧间隔引起的质量下降，从而在保持渲染质量的同时提升重建吞吐量。在真实世界数据集上的实验证实了TrackerSplat在具有显著位移的挑战性场景中的鲁棒性，其在并行设置下实现了优于基线方法的吞吐量并保持了视觉质量。代码见https://github.com/yindaheng98/TrackerSplat。