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PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence

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arxiv 2411.16877 v1 pith:PVPLR5MG submitted 2024-11-25 cs.CV

PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence

Zequn Chen , Jiezhi Yang , Heng Yang This is my paper
classification cs.CV
keywords pref3rgaussiannovel-viewsequencefeed-forwardpose-freeenablingfield
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present PreF3R, Pose-Free Feed-forward 3D Reconstruction from an image sequence of variable length. Unlike previous approaches, PreF3R removes the need for camera calibration and reconstructs the 3D Gaussian field within a canonical coordinate frame directly from a sequence of unposed images, enabling efficient novel-view rendering. We leverage DUSt3R's ability for pair-wise 3D structure reconstruction, and extend it to sequential multi-view input via a spatial memory network, eliminating the need for optimization-based global alignment. Additionally, PreF3R incorporates a dense Gaussian parameter prediction head, which enables subsequent novel-view synthesis with differentiable rasterization. This allows supervising our model with the combination of photometric loss and pointmap regression loss, enhancing both photorealism and structural accuracy. Given a sequence of ordered images, PreF3R incrementally reconstructs the 3D Gaussian field at 20 FPS, therefore enabling real-time novel-view rendering. Empirical experiments demonstrate that PreF3R is an effective solution for the challenging task of pose-free feed-forward novel-view synthesis, while also exhibiting robust generalization to unseen scenes.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    GaussLock embeds traps targeting position, scale, rotation, opacity, and color in 3D Gaussian models to degrade unauthorized fine-tunes while preserving authorized performance.

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