3D Scene Reconstruction from a Single Image Using Ray Casting
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Solution Overview
Problem
Existing methods for 3D scene reconstruction from a single 2D image are time-consuming and expensive, often requiring multiple images or stereo vision, and lack efficient tools for virtual staging in real estate and furniture advertising.
Innovation Solution
A system utilizing AI techniques and computer vision to isolate structural elements, employing semantic object removal, LAMA algorithm for mask generation, and ray-casting for spatial depth inference to create a fully enclosed 3D scene with architectural elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional physical staging is used to improve property appearance, then buyer perception and impression are improved, but cost, time consumption, and labor intensity increase
Solution Approach 1:
The patent creates virtual copies of physical furniture and decor items through 3D models, allowing virtual staging that replicates the visual effect of physical staging without the associated costs and time consumption. The system generates photorealistic rendered images that copy the appearance of staged spaces.
Solution Approach 2:
The patent replaces the mechanical process of physically moving and arranging furniture with an automated computer vision system that processes images and generates 3D reconstructions and virtual staging through software algorithms, eliminating manual labor.
2Manufacturing precision
If manual 3D scene creation is used for virtual staging, then scene detail and quality are improved, but time consumption and cost increase
Solution Approach 1:
The system performs automatic self-service by using computer vision algorithms to autonomously reconstruct 3D scenes from input images, identify furniture objects, and generate virtual staging without requiring manual 3D modeling expertise or time-intensive manual creation processes.
Solution Approach 2:
The patent replaces manual 3D modeling operations with automated machine learning algorithms that perform scene reconstruction, object detection, and virtual furniture placement through software, dramatically reducing creation time while maintaining quality.
3Measurement precision
If software requiring multiple images is used for 3D reconstruction, then depth accuracy is improved, but image availability and process simplicity worsen
Solution Approach 1:
The patent uses a single image (partial action) rather than requiring multiple images, achieving sufficient depth estimation through monocular depth estimation techniques and ray casting algorithms that infer the third dimension from limited visual data.
Solution Approach 2:
The system changes the input parameter from multiple images to a single image, using AI-based monocular depth estimation to compensate for the reduced input data, thereby maintaining adaptability while achieving acceptable depth reconstruction accuracy.
4Adaptability or versatility
If conventional virtual staging software is used, then staging capability is provided, but ease of operation and accessibility worsen
Solution Approach 1:
The system provides self-service virtual staging by automatically processing uploaded images through computer vision and machine learning pipelines, eliminating the need for users to manually create 3D models or configure complex software parameters, thereby greatly improving ease of operation.
Solution Approach 2:
The patent replaces complex manual software operations with automated AI-based image processing that handles scene understanding, 3D reconstruction, and furniture placement automatically, making the system accessible to non-expert users.
Data Source
AI summary
A system and method is provided for reconstructing a 3D scene from a single 2D image. In one embodiment, AI techniques and computer vision is used to isolate structural elements from non-structural ones. Semantic object removal precedes a process that translates 2D image coordinates into a 3D modeling-compatible coordinate system. A floor mask is generated, followed by a point filtering process that optimizes data for structured mesh generation. A virtual camera and ray-casting techniques infer spatial depth, enabling the creation of a fully enclosed 3D scene with architectural elements such as walls and windows. The 3D scene can then be populated, or staged, to include virtual non-structural elements (e.g., couches, tables, etc.).