3D Environment Reconstruction Using Planar Constraints
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Solution Overview
Problem
Current artificial reality systems face challenges in accurately and efficiently constructing 3D virtual representations of real-world environments from 2D images due to noise and computational inefficiencies in processing individual 3D points, leading to inaccurate and resource-intensive mapping processes.
Innovation Solution
The system identifies and collapses subsets of points into cohesive planar surfaces, using these surfaces as constraints to estimate camera positions and generate a 3D virtual map, thereby reducing noise and conserving processing power by representing environments with fewer, more accurate planes rather than individual points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual 3D points are processed to construct the environment map, then the detail representation is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent merges multiple 3D points that lie on the same planar surface into a single cohesive plane representation. Instead of processing each individual point, the system identifies coplanar points and consolidates them into plane objects with mathematical representations (e.g., ax+by+cz+d=0), dramatically reducing computational complexity while preserving the essential geometric information of the environment.
Solution Approach 2:
The patent transforms the representation parameters from individual point coordinates (x, y, z) to plane parameters (normal vector components a, b, c and distance d). This parameter transformation allows the system to describe entire surfaces with a fixed number of parameters regardless of the number of points, reducing the dimensional complexity of the data structure.
2Loss of information
If all individual 3D points are stored and processed, then the complete environmental information is preserved, but the memory usage and processing power requirements increase
Solution Approach 1:
The system combines multiple individual point data into unified plane representations. By identifying that many points share the same planar geometry, the system stores one plane object instead of numerous point objects, significantly reducing memory consumption while maintaining the ability to represent the complete environmental structure through the collection of plane objects.
3Measurement precision
If noise from individually estimated 3D point positions is considered, then the detailed position data is retained, but the accuracy of the planar surface representation decreases
Solution Approach 1:
The patent changes the approach from using individual noisy point estimates to fitting plane parameters that best represent the underlying planar surface. By using mathematical optimization to determine plane parameters (normal vector and distance) from multiple points, the system filters out random noise and produces more accurate planar surface representations that capture the true geometry.
4Manufacturing precision
If the system processes every point in the point cloud, then the thoroughness of environment reconstruction is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the point cloud data into distinct planar regions through plane detection algorithms. By dividing the continuous point cloud into discrete planar segments, the system can process each plane independently and efficiently, avoiding the need to handle every individual point. This segmentation enables faster processing while maintaining reconstruction accuracy through the use of plane constraints in the reconstruction pipeline.
Data Source
AI summary
An artificial reality system is configured to more accurately and efficiently construct a 3D virtual representation of a real-world environment from a set of 2D images. The system identifies points and/or lines within the images that define a plane along an orientation and then performs a planar sweep along a perpendicular path to identify surfaces in which the plane intersects with multiple points. Points that appear to be in the same plane are “collapsed” into a cohesive plane to conserve processing power by estimating and/or storing parameters for the cohesive plane, rather than all of the individual 3D points. In this way, the system also “averages out” random variation in the planar surface that would otherwise result from random noise in the estimation of the individual 3D points. The system may then generate a 3D map from a constrained alignment of all of the identified planes.