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DrawSplat: Atlanta World-Guided Implicit Gaussian SLAM for Indoor Reconstruction

DrawSplat proposes an Atlanta World-guided implicit Gaussian SLAM framework for indoor reconstruction. The method first estimates camera poses, sparse geometry, and gravity direction through joint point-line tracking, then infers structural planes from tracked lines using the shared gravity component of the Atlanta World prior and converts them into differentiable regularization terms on Gaussian centers and normals. The framework also combines an anchor-based implicit neural Gaussian representation with adaptive structural sampling, achieving robust tracking, high-fidelity rendering, and geometrically consistent reconstruction across multiple datasets.

Part of the event: DrawSplat: Implicit Gaussian SLAM Guided by Atlanta World

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