StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams
This addresses the problem of real-time 3D scene reconstruction for applications like robotics and AR, representing a novel method for a known bottleneck.
The paper tackled real-time dynamic 3D reconstruction from uncalibrated video streams by introducing StreamSplat, which outperformed prior works in reconstruction quality and dynamic scene modeling while enabling online processing of arbitrarily long videos.
Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams is crucial for numerous real-world applications. However, existing methods struggle to jointly address three key challenges: 1) processing uncalibrated inputs in real time, 2) accurately modeling dynamic scene evolution, and 3) maintaining long-term stability and computational efficiency. To this end, we introduce StreamSplat, the first fully feed-forward framework that transforms uncalibrated video streams of arbitrary length into dynamic 3D Gaussian Splatting (3DGS) representations in an online manner, capable of recovering scene dynamics from temporally local observations. We propose two key technical innovations: a probabilistic sampling mechanism in the static encoder for 3DGS position prediction, and a bidirectional deformation field in the dynamic decoder that enables robust and efficient dynamic modeling. Extensive experiments on static and dynamic benchmarks demonstrate that StreamSplat consistently outperforms prior works in both reconstruction quality and dynamic scene modeling, while uniquely supporting online reconstruction of arbitrarily long video streams. Code and models are available at https://github.com/nickwzk/StreamSplat.