FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction

• URL: http://arxiv.org/abs/2412.09573v1
• Date: Thu, 12 Dec 2024 18:52:53 GMT
• Title: FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction
• Authors: Jiale Xu, Shenghua Gao, Ying Shan,
• Abstract summary: We present FreeSplatter, a feed-forward reconstruction framework capable of generating high-quality 3D Gaussians from sparse-view images. FreeSplatter is built upon a streamlined transformer architecture, comprising sequential self-attention blocks. We show FreeSplatter's potential in enhancing the productivity of downstream applications, such as text/image-to-3D content creation.
• Score: 59.77970844874235
• License:
• Abstract: Existing sparse-view reconstruction models heavily rely on accurate known camera poses. However, deriving camera extrinsics and intrinsics from sparse-view images presents significant challenges. In this work, we present FreeSplatter, a highly scalable, feed-forward reconstruction framework capable of generating high-quality 3D Gaussians from uncalibrated sparse-view images and recovering their camera parameters in mere seconds. FreeSplatter is built upon a streamlined transformer architecture, comprising sequential self-attention blocks that facilitate information exchange among multi-view image tokens and decode them into pixel-wise 3D Gaussian primitives. The predicted Gaussian primitives are situated in a unified reference frame, allowing for high-fidelity 3D modeling and instant camera parameter estimation using off-the-shelf solvers. To cater to both object-centric and scene-level reconstruction, we train two model variants of FreeSplatter on extensive datasets. In both scenarios, FreeSplatter outperforms state-of-the-art baselines in terms of reconstruction quality and pose estimation accuracy. Furthermore, we showcase FreeSplatter's potential in enhancing the productivity of downstream applications, such as text/image-to-3D content creation.

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