Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting

A new arXiv paper (2609.13262v1) proposes a holistic framework for 3D Gaussian Splatting scene extrapolation and interpolation that replaces the error-prone generate-reconstruct-shift cycle with an independent camera view detection mechanism enabling parallel, conflict-free extrapolation. The framework adds a hierarchical pipeline that handles independent and dependent camera views separately, plus a plug-and-play Quality-Aware Mask (QA-Mask) module that calibrates learning weights by pixel-wise rendering quality to prevent generation-induced degradation of well-reconstructed areas. The authors report superior performance in extensive experiments, with QA-Mask generalizing to multiple generative reconstruction models.

arXiv:2609.13262v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exhibiting holes in unobserved regions and artifacts in observable areas. Recent works formulate this task as extrapolation and interpolation and try to address it with generative models, but remain limited in extrapolation scale and quality. They repeat a generate-reconstruct-shift cycle to progressively build a scene, which introduces accumulated errors with every step conditioning on previous outcomes. In this work, we propose a holistic framework for extrapolation and interpolation. We devise an independent camera view detection mechanism to enable parallel conflict-free extrapolation, circumventing the reliance on the aforementioned error-prone cycle. Building upon this, we design a hierarchical pipeline that extrapolates independent and dependent camera views separately. Additionally, previous methods overlook inconsistency between generated and original images, resulting in compromising well-reconstructed areas. We propose a plug-and-play Quality-Aware Mask QA-Mask module, enabling selective utilization of generated data. By calibrating learning weights with pixel-wise rendering quality, it prevents generation-induced degradations on well-constructed areas. Extensive experiments demonstrate the superior performance of our framework, with QA-Mask generalizing to multiple generative reconstruction models.