Room Layout Estimation Using Prediction Planes for Sparse SLAM
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Solution Overview
Problem
Existing computer vision technologies, such as SLAM, provide sparse representations of physical environments due to limitations in sample points, and room layout estimation methods often rely on predefined assumptions that can lead to inaccuracies in estimating curved surfaces and scaling issues.
Innovation Solution
A room layout estimation engine uses disjunctive normal models to generate prediction planes from extracted coefficients, combining them to create a complete room layout without relying on predefined assumptions, and integrates with VIO SLAM systems to enhance accuracy and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SLAM uses a limited number of sample points to determine orientation and position, then computational efficiency is maintained, but the geometrical model becomes sparse and incomplete
Solution Approach 1:
The patent segments the room layout estimation into multiple components: detecting corners/edges, identifying planar surfaces, estimating room dimensions, and generating a complete layout representation. This segmentation allows the system to process information in manageable steps while building a comprehensive model from discrete features.
Solution Approach 2:
The patent transitions from 2D image processing to 3D spatial representation by extracting planar surfaces and constructing a three-dimensional room model. This dimensional transformation enables the system to represent the complete physical environment accurately while maintaining computational efficiency through optimized algorithms.
2Device complexity
If room layout estimation relies on predefined assumptions, then computational complexity is reduced, but accuracy in estimating curved surfaces and scaling is compromised
Solution Approach 1:
The patent changes the parameters used for surface estimation from fixed predefined assumptions to adaptive parameters that can model curved surfaces. The system estimates surface curvature and adjusts its representation accordingly, enabling accurate modeling of non-planar surfaces while controlling computational complexity through optimized parameter transformation.
Solution Approach 2:
The patent explicitly models curved surfaces by detecting and representing them as non-planar geometries. The system identifies curved surfaces in the physical environment and represents them appropriately in the estimated room layout, eliminating the need for predefined flat surface assumptions and improving estimation accuracy.
3Speed
If room layout estimation uses only 2D image frames, then processing speed is maintained, but the ability to represent complete 3D room geometries is limited
Solution Approach 1:
The patent transforms 2D image data into 3D room representations by extracting planar surfaces and inferring spatial relationships. The system uses the 2D image frame to detect corners, edges, and surfaces, then reconstructs the corresponding 3D geometry, achieving complete room layout representation while maintaining processing efficiency through optimized algorithms.
Solution Approach 2:
The patent performs preliminary 2D image processing and feature extraction before constructing the 3D model. By pre-processing the image frame to identify corners, edges, and planar surfaces, the system prepares structured data that facilitates efficient 3D reconstruction, maintaining processing speed while improving geometric accuracy.
Data Source
AI summary
Systems, methods, and computer readable media to implementing an end-to-end room layout estimation are described. A room layout estimation engine performs feature extraction on an image frame to generate a first set of coefficients for a first room layout class and a second set of coefficients for a second room layout class. Afterwards, the room layout estimation engine generates a first set of planes according to the first set of coefficients and a second set of planes according to the second set of coefficients. The room layout estimation engine generates a first prediction plane according to the first set of planes and a second prediction plane according to the second set of planes. Afterwards, the room layout estimation engine merges the first prediction plane and the second prediction plane to generate a predicted room layout for the room.