Scenix: Sparse-View 3D Scene Reconstruction via Executable Scene Programs

• URL: http://arxiv.org/abs/2608.07012v1
• Date: Fri, 07 Aug 2026 09:24:56 GMT
• Title: Scenix: Sparse-View 3D Scene Reconstruction via Executable Scene Programs
• Abstract summary: We present textscScenix, a sparse-view 3D scene reconstruction framework via executable scene programs.<n>Given sparse views, textscScenix predicts executable scene programs through perception-grounded asset instantiation and closed-loop spatial refinement.<n>We further introduce observation-consistent supervision that aligns each target scene with the visual evidence available in its input views.
• Score: 36.87702450793552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthesizing a structured and editable 3D indoor scene from a few uncalibrated RGB views requires more than generating high-quality individual assets: a system must infer the room structure, associate objects across incomplete observations, and recover a globally consistent spatial configuration. Previous methods mainly focus on 3D scene generation with text input or require continuous visual inputs with additional priors, \ e.g., human-annotated masks or accurate 3D layouts, which makes these methods labor demanding and hard to apply in general cases. We present \textsc{Scenix}, a sparse-view 3D scene reconstruction framework via executable scene programs, a structured representation that can be directly instantiated into editable 3D scenes. Given sparse views, \textsc{Scenix} predicts executable scene programs through perception-grounded asset instantiation and closed-loop spatial refinement. % We present \method, a framework that predicts an executable scene representation from sparse views and realizes it through perception-grounded asset instantiation and closed-loop spatial refinement. To support this task, we construct \dataset, a dataset of approximately 110,000 synthetic and real indoor scenes with multiview imagery, room structures, object-centric descriptions, and metric spatial annotations. We further introduce observation-consistent supervision that aligns each target scene with the visual evidence available in its input views. Experiments on held-out \textsc{XScene} scenes, real indoor images, and out-of-distribution SpatialGen cases evaluate structured scene prediction, object grounding, and spatial refinement.

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