SpatialVID
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- PKU, VGI Labs and UC Merced unveil DynStream for pose-free streaming 4D reconstruction
A team from Peking University's Wang Xuan Institute of Computer Technology, VGI Labs and UC Merced has proposed DynStream, an online streaming 4D Gaussian feed-forward reconstruction framework. It takes an RGB online video stream with no camera poses and feed-forward outputs a camera trajectory plus a continuously updated, renderable dynamic scene representation: video is processed in temporal chunks, local reconstructions are merged into a shared world frame using predicted relative poses and a per-chunk scale factor, and Gaussians are split into static and dynamic by a predicted dynamic probability. The authors report better rendering metrics than feed-forward baselines such as AnySplat, MVSplat and NeoVerse across five datasets including Waymo and SpatialVID, and a Sim(3) ATE reduced from hundreds of meters to 40 m on 11 KITTI sequences totaling 23,201 frames and up to 5 km long. These figures are author-reported and no independent replication is provided.
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