AI-Guided Point Cloud Capture for Outlier Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Image sensors in user devices capture images with low resolutions and often suffer from blur phenomena, and depth information measurement technologies are underdeveloped, limiting effective point cloud generation applications such as 3D scanning, 3D printing, virtual reality, and autonomous driving.
Innovation Solution
An electronic device uses sensors like LiDAR, ToF, and RGB-D sensors to generate a point cloud, identifies outlier points using artificial intelligence models, and provides a re-photographing location guide to improve point cloud completeness by adjusting the photographing position or angle based on identified outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If image sensors are made smaller to fit in user devices, then device size is reduced, but image resolution deteriorates and blur phenomena occur
Solution Approach 1:
The patent introduces depth information as an intermediary element to compensate for the poor quality of images captured by small sensors. By combining RGB images with depth maps from sensors like ToF or LiDAR, the system creates a multi-dimensional representation that overcomes the resolution limitations of small image sensors.
Solution Approach 2:
The patent combines multiple types of sensing data (RGB images, depth maps, point clouds) to create a composite representation of the scene. This composite approach allows the system to leverage the strengths of different sensors to compensate for the weaknesses of individual small sensors.
2Device complexity
If depth information measurement technologies are underdeveloped, then device complexity is reduced, but point cloud generation quality deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system evaluates the quality of generated point clouds, identifies outlier points that violate predefined rules, and uses this information to guide re-photographing. This feedback loop continuously improves point cloud quality without requiring increasingly complex depth measurement hardware.
Solution Approach 2:
The patent performs preliminary evaluation of point cloud quality and identifies areas needing improvement before final processing. By detecting outlier points and generating re-photographing location guides in advance, the system prepares targeted improvements rather than relying on overly complex real-time depth measurement.
3Ease of manufacture
If sensors capture images with low resolution, then device cost is reduced, but point cloud completeness deteriorates
Solution Approach 1:
The patent transitions from two-dimensional image data to three-dimensional point cloud representation by incorporating depth information. This dimensional transformation allows the system to recover information lost in low-resolution 2D images by adding the depth dimension, effectively compensating for sensor limitations without increasing cost.
Solution Approach 2:
The patent performs preliminary depth estimation and point cloud generation from low-resolution images, then uses AI models to identify missing or inaccurate regions. This preliminary processing allows the system to work with low-resolution input while systematically identifying and correcting information gaps.
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
Enhances the accuracy and completeness of point clouds by correcting violations of predefined rules, ensuring a more detailed and reliable representation of objects for applications like 3D scanning and virtual reality.
Implementation Method 1
light detection and ranging (LiDAR) sensors
Implementation Method 2
light detection and ranging (LiDAR) sensors
Implementation Method 3
Time-of-Flight (ToF) sensors
Data Source
AI summary
Provided are a method and an electronic device for generating a point cloud. The method includes obtaining, from at least one sensor of the electronic device, first sensing data corresponding to an object, obtaining a first point cloud corresponding to the object, based on the first sensing data, identifying, by using at least one artificial intelligence model, at least one outlier point indicating violation of at least one predefined rule in the first point cloud, and providing a re-photographing location guide for re-photographing the object, based on the at least one outlier point.