3D Room Mesh Generation with Layout Constraints
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Solution Overview
Problem
Existing text-driven 3D indoor scene generation methods fail to accurately capture room layouts and lack the ability to flexibly edit individual objects within the room, resulting in unconvincing results that lack semantic plausibility.
Innovation Solution
A two-stage method for 3D indoor scene generation is introduced, consisting of a layout generation stage and an appearance generation stage. The layout generation stage uses a text-conditioned diffusion model to learn the layout distribution, while the appearance generation stage employs a Control Net model to generate a vivid panoramic image, allowing for the creation of high-quality 3D rooms with realistic layouts and textures. Additionally, a mask-guided editing method enables users to interactively adjust the size, placement, and semantic class of furniture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If incremental framework is used to generate 3D indoor scenes frame-by-frame, then the generation process can be simplified, but the global layout consistency and semantic plausibility deteriorate
Solution Approach 1:
The patent divides the scene generation process into two independent stages: layout generation (generating spatial structure and object positions) and appearance generation (generating visual details). This segmentation allows each stage to focus on its specific task, ensuring global layout consistency in the layout stage while maintaining generation simplicity through modular processing.
Solution Approach 2:
The patent performs layout generation as a preliminary action before appearance generation. By first establishing the global spatial structure and object placements in the layout stage, the system ensures semantic plausibility and layout consistency is built into the foundation before visual details are added, preventing Penrose Triangle problems.
2Productivity
If text-driven generation is used to create 3D indoor scenes, then the creation time is reduced, but the ability to edit individual objects deteriorates
Solution Approach 1:
The patent separates scene representation into independent layout components (spatial structure, object positions) and appearance components (visual details). This segmentation enables selective editing of individual objects by modifying only their specific parameters in the layout representation, while maintaining the overall scene integrity and rapid generation capabilities.
Solution Approach 2:
The patent represents scenes using parameterized layout structures where individual object properties (position, size, orientation) are explicit parameters. This allows users to quickly edit specific objects by changing their parameters without regenerating the entire scene, combining fast text-driven generation with flexible object-level editing.
3Extent of automation
If existing text-driven methods are used for 3D indoor scene generation, then the process is automated, but the semantic plausibility and layout合理性 deteriorate
Solution Approach 1:
The patent performs preliminary layout generation that explicitly models spatial relationships and semantic constraints before appearance generation. This preliminary structuring of semantic information ensures that automated generation produces semantically plausible scenes by establishing correct object placements and spatial configurations in advance.
Solution Approach 2:
The patent introduces a layout representation as an intermediary between text input and final 3D scene generation. This intermediary structure encodes semantic knowledge and spatial constraints, acting as a mediator that guides the automated generation process to produce semantically plausible results while maintaining automation efficiency.
Data Source
AI summary
A system for generating 3D indoor scenes from text input is provided. It includes a user interface, a text processing module, a scene code generator, a layout generation module, an appearance generation module, a NeRF module, and a PeRF module. The user inputs text describing a room, which is processed into scene codes. These codes guide the generation of a 3D layout using oriented bounding boxes, ensuring spatial integrity. The appearance module then creates a visual representation with a panoramic image. The NeRF module constructs a base 3D model, which is refined by the PERF module for enhanced visual coherence.