ScanEdit: Hierarchically-Guided Functional 3D Scan Editing
Boudjoghra, Mohamed El Amine Laptev, Ivan Dai, Angela
Boudjoghra, Mohamed El Amine
Laptev, Ivan
Dai, Angela
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Computer Vision
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Conference proceeding
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Abstract
With the fast pace of 3D capture technology and resulting abundance of 3D data, effective 3D scene editing becomes essential for a variety of graphics applications. In this work we present ScanEdit, an instruction-driven method for functional editing of complex, real-world 3D scans. To model large and interdependent sets of objects, we propose a hierarchically-guided approach. Given a 3D scan decomposed into its object instances, we first construct a hierarchical scene graph representation to enable effective, tractable editing. We then leverage reasoning capabilities of Large Language Models (LLMs) and translate highlevel language instructions into actionable commands applied hierarchically to the scene graph. Finally, ScanEdit integrates LLM-based guidance with explicit physical constraints and generates realistic scenes where object arrangements obey both physics and common sense. In our extensive experimental evaluation ScanEdit outperforms state of the art and demonstrates excellent results for a variety of real-world scenes and input instructions. Our code is available at aminebdj.github.io/scanedit
Citation
M.E.A. Boudjoghra, I. Laptev, A. Dai, "ScanEdit: Hierarchically-Guided Functional 3D Scan Editing," 2026, pp. 27105-27115.
Source
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Conference
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Keywords
46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games
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Source
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE