Depth Image Interpolation for Shiny and Dark Item Recognition
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Solution Overview
Problem
Existing depth interpolation methods struggle with accurately capturing depth information from shiny or dark objects, leading to incorrect or missing depth values, which hinders multiview item recognition in transaction areas.
Innovation Solution
A system and method that adjusts camera settings, employs IR light emission patterns, uses masking techniques, and applies depth interpolation algorithms to enhance and replace poor-quality depth values, combined with a KNN lookup process for accurate depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depth interpolation methods are used, then processing speed is maintained, but depth measurement precision deteriorates for shiny or dark objects
Solution Approach 1:
The system performs preliminary classification of depth values into sufficient and insufficient categories before interpolation. By identifying poor-quality depth readings (from shiny or dark objects) in advance and separating them from reliable readings, the system can apply appropriate processing strategies to each category, improving overall depth measurement precision without uniformly complicating the entire processing pipeline.
Solution Approach 2:
The patent applies different processing approaches to different regions of the depth image based on local quality assessment. Sufficient depth values undergo standard interpolation, while insufficient depth values (from challenging surfaces) receive specialized handling including model-based replacement. This localized quality-based processing resolves the contradiction by tailoring the complexity of processing to the specific needs of each region.
2Measurement precision
If depth values are replaced with model values, then depth measurement precision improves for challenging objects, but loss of information increases when original depth data is discarded
Solution Approach 1:
The system applies model value replacement selectively rather than universally. Only depth values classified as insufficient (from shiny or dark objects) are replaced with model predictions, while sufficient depth values from reliable surfaces are preserved. This partial action approach maintains measurement precision for challenging objects while minimizing information loss by retaining authentic depth data where available.
Solution Approach 2:
The patent introduces model depth values as an intermediary solution for regions where direct measurement fails. Rather than completely discarding original depth data, the system uses model predictions to supplement and correct insufficient measurements, creating a hybrid depth map that combines authentic sensor data with synthesized information. This intermediary approach balances precision improvement with information preservation.
3Measurement precision
If multiple depth interpolation algorithms are applied, then depth image quality improves, but processing time increases
Solution Approach 1:
The patent segments the depth processing task into distinct stages: classification of depth value quality, selective application of interpolation algorithms, and model-based replacement for insufficient values. By dividing the processing pipeline into specialized segments rather than applying all algorithms uniformly to all pixels, the system improves depth image quality through multiple algorithms where needed while minimizing processing time by avoiding unnecessary computations in regions with sufficient data.
4Measurement precision
If camera settings are adjusted and IR light patterns are used, then depth measurement precision improves for dark and shiny objects, but device complexity increases
Solution Approach 1:
The system dynamically adjusts camera parameters including IR light emission patterns and exposure settings to optimize depth measurements for challenging surfaces. By changing operational parameters rather than adding complex hardware, the patent improves depth measurement precision for dark and shiny objects while minimizing increases in device complexity. The adjustable parameters allow the same camera system to adapt to different object types without requiring multiple specialized devices.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves depth image quality by mitigating noise, missing, and erroneous values, enabling precise item recognition and location within transaction areas, even with challenging object surfaces.
Implementation Method 1
One example of such a camera is the LiDAR (Light Detection And Ranging). Lasers are used to measure distances and obtain depths of surfaces from the objects captured by the camera/scanner.
Implementation Method 2
a separate image is captured of Red-Green-Blue (RGB) light reflected from the objects as a 2D (two-dimensional) object
Data Source
AI summary
Depth camera settings are adjusted based on characteristics of items presented in a scan zone and based on depth values returned for the items in depth images. Red-Green-Blue (RGB) images and depth images are captured of items within the scan zone. Quality of the depth values are accessed. Bad depth values are replaced with good known depth values. When the depth values are not replaced, one or more depth value interpolation algorithms are selectively processed to enhance the depth values. The depth values are processed to place each item within a specific location of the scan zone and map that location to pixel values in the corresponding RGB images. The pixel values from the RGB images are passed to a classification model and an item code is returned. The item codes are provided to checkout a customer without any scanning of item barcodes of the items.