AI RESEARCH
TokenGS: Decoupling 3D Gaussian Prediction from Pixels with Learnable Tokens
arXiv CS.CV
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ArXi:2604.15239v1 Announce Type: new In this work, we revisit several key design choices of modern Transformer-based approaches for feed-forward 3D Gaussian Splatting (3DGS) prediction. We argue that the common practice of regressing Gaussian means as depths along camera rays is suboptimal, and instead propose to directly regress 3D mean coordinates using only a self-supervised rendering loss.