StreamSplat, a fully feed-forward framework, addresses real-time 3D scene reconstruction from uncalibrated video with accurate dynamics and long-term stability.
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.