Kavli Affiliate: Wei Gao
| First 5 Authors: Junhong Lin, Junhong Lin, , ,
| Summary:
Feed-forward surround-view autonomous driving scene reconstruction offers
fast, generalizable inference ability, which faces the core challenge of
ensuring generalization while elevating novel view quality. Due to the
surround-view with minimal overlap regions, existing methods typically fail to
ensure geometric consistency and reconstruction quality for novel views. To
tackle this tension, we claim that geometric information must be learned
explicitly, and the resulting features should be leveraged to guide the
elevating of semantic quality in novel views. In this paper, we introduce
textbfVisual Gaussian Driving (VGD), a novel feed-forward end-to-end
learning framework designed to address this challenge. To achieve generalizable
geometric estimation, we design a lightweight variant of the VGGT architecture
to efficiently distill its geometric priors from the pre-trained VGGT to the
geometry branch. Furthermore, we design a Gaussian Head that fuses multi-scale
geometry tokens to predict Gaussian parameters for novel view rendering, which
shares the same patch backbone as the geometry branch. Finally, we integrate
multi-scale features from both geometry and Gaussian head branches to jointly
supervise a semantic refinement model, optimizing rendering quality through
feature-consistent learning. Experiments on nuScenes demonstrate that our
approach significantly outperforms state-of-the-art methods in both objective
metrics and subjective quality under various settings, which validates VGD’s
scalability and high-fidelity surround-view reconstruction.
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