Normal Estimation Using Gravity Vector and Superpixels
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Solution Overview
Problem
Normal estimation in machine vision is a resource-intensive operation prone to errors, particularly when performed on a pixel-by-pixel basis for identifying planar surfaces in environments.
Innovation Solution
The method involves using a directional measurement, such as a gravity vector, to determine normal vectors for planar surfaces by identifying surfaces parallel or orthogonal to the directional vector, reducing the need for computationally intensive techniques and enhancing operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel normal estimation is performed to identify planar surfaces, then measurement precision is improved, but use of energy increases and productivity decreases
Solution Approach 1:
The image is divided into superpixels that group adjacent pixels with similar characteristics. Instead of processing each pixel individually for normal estimation, the method processes entire superpixels as unified units. This segmentation reduces the total number of processing units from thousands of individual pixels to hundreds or dozens of superpixels, dramatically reducing computational energy consumption while preserving normal estimation accuracy for planar surfaces.
Solution Approach 2:
The superpixel generation process serves multiple functions simultaneously: it groups pixels for efficient processing, preserves edge information through boundary-aware clustering, and creates uniform regions suitable for normal estimation. This multi-functional approach eliminates the need for separate preprocessing steps while maintaining measurement precision.
2Measurement precision
If pixel-by-pixel normal estimation is performed to identify planar surfaces, then measurement precision is improved, but productivity decreases
Solution Approach 1:
By segmenting the image into superpixels, the method reduces the number of independent processing units from the total pixel count to a much smaller number of superpixels. Each superpixel represents a coherent region that can be processed as a single unit for normal estimation, significantly increasing processing speed while maintaining accuracy through the preservation of edge boundaries and uniform regional characteristics.
Solution Approach 2:
The superpixels are generated in advance before normal estimation is performed. This preliminary segmentation organizes the pixel data into ready-to-process units with defined boundaries and uniform characteristics, eliminating the need for repeated pixel-level analysis during the normal estimation phase and thereby improving overall processing productivity.
3Measurement precision
If computationally intensive normal estimation techniques are used, then measurement precision is improved, but heat generation increases
Solution Approach 1:
The segmentation into superpixels reduces the computational workload by grouping pixels into larger units that can be processed more efficiently. This reduction in the number of processing operations directly decreases the energy consumption and associated heat generation from the processor, while the superpixel methodology preserves the accuracy needed for reliable normal estimation of planar surfaces.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods for normal estimation using a directional measurement, such as a gravity vector. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes identifying planar surfaces in an environment represented by an image. Each planar surface is associated with a respective orientation. A directional vector associated with the environment is determined. A subset of the planar surfaces that have a threshold orientation relative to the directional vector is identified. For each planar surface in the subset of the planar surfaces, a normal vector for the planar surface is determined based on the orientation of the planar surface and the directional vector.