S2Tok
A feed-forward framework for streaming 3D Gaussian reconstruction that maintains an adaptively expandable scene state through persistent spatial tokens. It combines a learned admission module and a hierarchical decoder to enable online 3D Gaussian reconstruction from uncalibrated image streams.
Timeline (last 90 days)
The arXiv paper "S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens" proposes a solution for streaming 3D Gaussian reconstruction.
S2Tok is a feed-forward framework whose core objective is to maintain an adaptively sized, persistent scene state from an uncalibrated image stream.
The central idea of S2Tok is to distinguish between updating existing representations and selective expansion.
S2Tok introduces a learnable Admission Module to selectively decide when to expand the representation.
S2Tok uses a hierarchical decoder and a Gaussian head to convert latent states into non-pixel-aligned 3D Gaussians.
Because the Gaussians are not bound to specific pixels, S2Tok can perform novel view synthesis without caching historical frames.
Researchers evaluated S2Tok on four benchmark datasets, and the results show that it achieves competitive streaming rendering quality while maintaining a compact Gaussian representation.
S2Tok's innovation lies in introducing the temporal dimension—the token set grows over time, and old tokens can be modified by new observations, serving as hidden states in a recurrent system.
S2Tok's generalization capability, performance in extremely large-scale scenes, and integration effects with existing SLAM systems still await validation in follow-up work.
All Facts (9)
S2Tok's generalization capability, performance in extremely large-scale scenes, and integration effects with existing SLAM systems still await validation in follow-up work.
50%UnverifiedS2Tok is a feed-forward framework whose core objective is to maintain an adaptively sized, persistent scene state from an uncalibrated image stream.
50%UnverifiedThe central idea of S2Tok is to distinguish between updating existing representations and selective expansion.
50%UnverifiedS2Tok introduces a learnable Admission Module to selectively decide when to expand the representation.
50%UnverifiedS2Tok uses a hierarchical decoder and a Gaussian head to convert latent states into non-pixel-aligned 3D Gaussians.
50%UnverifiedBecause the Gaussians are not bound to specific pixels, S2Tok can perform novel view synthesis without caching historical frames.
50%UnverifiedResearchers evaluated S2Tok on four benchmark datasets, and the results show that it achieves competitive streaming rendering quality while maintaining a compact Gaussian representation.
50%UnverifiedS2Tok's innovation lies in introducing the temporal dimension—the token set grows over time, and old tokens can be modified by new observations, serving as hidden states in a recurrent system.
50%UnverifiedThe arXiv paper "S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens" proposes a solution for streaming 3D Gaussian reconstruction.
50%