Piecewise Planar 3D Scene Reconstruction
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Solution Overview
Problem
Conventional rendering engines face issues in generating high-quality, navigable, and photo-realistic three-dimensional scenes from two-dimensional images, particularly for architectural and urban scenes with man-made objects, due to errors in rendering textureless surfaces, non-Lambertian surfaces, and occlusions, leading to poor detail and quality.
Innovation Solution
A computerized reconstruction system that identifies discrete three-dimensional plane candidates using sparse point clouds and line segments from multiple views, employing multi-label Markov Random Field optimization and graph-cut techniques to generate piecewise planar depth maps, which are then used to project and render the three-dimensional scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional rendering engines use interpolation to cover holes in textureless or non-Lambertian surfaces, then the scene structure can be completed, but the surface accuracy deteriorates and flat surfaces are erroneously reproduced as bumpy surfaces
Solution Approach 1:
The patent segments the scene into discrete planar surfaces identified through line segment extraction and plane fitting. Instead of treating the scene as a continuous field requiring interpolation, the method divides it into distinct planar regions that can be accurately represented without interpolation, thereby maintaining surface accuracy while achieving scene completeness.
Solution Approach 2:
The patent performs preliminary action by extracting line segments and fitting planes before rendering. This pre-processing step identifies the geometric structure of the scene in advance, allowing the rendering engine to accurately represent surfaces based on pre-determined plane equations rather than interpolating during rendering, thus avoiding the bumpy surface artifact.
2Loss of information
If conventional rendering engines perform feature matching on non-Lambertian surfaces and occlusions, then more scene details can be recovered, but matching reliability deteriorates and jaggies are introduced
Solution Approach 1:
The patent extracts line segments as distinctive geometric features from images. Line segments are more reliable for matching than general texture features because they represent explicit geometric boundaries that are invariant to lighting conditions and material properties, thereby maintaining matching reliability while recovering scene details.
Solution Approach 2:
The patent applies local quality by using plane fitting within local neighborhoods of line segments. Each plane is fitted locally based on nearby line segments, allowing accurate representation of local geometric structures without requiring unreliable global matching across occlusions and non-Lambertian surfaces, thus avoiding jaggies while recovering details.
3Ease of operation
If conventional rendering engines use sparse point cloud triangulation, then the three-dimensional structure can be reconstructed, but the quality and detail of the generated scenes deteriorate
Solution Approach 1:
The patent segments the point cloud into regions associated with identified planes. Instead of uniformly triangulating the entire sparse point cloud, the method divides it into planar regions and represents each region using its corresponding plane equation, thereby maintaining reconstruction simplicity while significantly improving scene quality through accurate planar representation.
Solution Approach 2:
The patent changes the representation parameters from individual point coordinates to plane equations (ax + by + cz + d = 0). This parameter change allows the system to represent entire planar surfaces with a single equation rather than requiring dense point sampling, thus maintaining operational simplicity while achieving high scene quality.
4Adaptability or versatility
If conventional rendering engines assume general surface models for man-made surfaces, then the algorithms can be universally applied, but the accuracy for planar architectural surfaces deteriorates
Solution Approach 1:
The patent applies local quality by adapting the surface model locally to match the actual geometry. Instead of using a single general surface model for the entire scene, the method identifies planar regions and applies plane fitting specifically to those regions, thereby maintaining algorithm versatility while achieving high accuracy for planar architectural surfaces.
Solution Approach 2:
The patent introduces dynamics by making the surface model adaptive rather than static. The system dynamically selects between different surface representations (planes vs. general surfaces) based on the local geometric characteristics detected from line segments, allowing the algorithm to maintain universality while achieving specialized accuracy for planar surfaces where applicable.
Data Source
AI summary
Methods, systems, and computer-readable media for reconstruction a three-dimensional scene from a collection of two-dimensional images are provided. A computerized reconstruction system executes computer vision algorithms on the collection of two-dimensional images to identify candidate planes that are used to model visual characteristics of the environment depicted in the two-dimensional images. The computer vision algorithms may minimize an energy function that represents the relationships and similarities among features of the two-dimensional images to assign pixels of the two dimensional images to planes in the three dimensional scene. The three-dimensional scene is navigable and depicts viewpoint transitions between multiple two-dimensional images.