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

• URL: http://arxiv.org/abs/2411.16877v1
• Date: Mon, 25 Nov 2024 19:16:29 GMT
• Title: PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence
• Authors: Zequn Chen, Jiezhi Yang, Heng Yang,
• Abstract summary: We present PreF3R, Pose-Free Feed-forward 3D Reconstruction from an image sequence of variable length. 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.
• Score: 3.61512056914095
• License:
• Abstract: 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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