Feature Embedding Using 3D Point Clouds from Depth Maps
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Solution Overview
Problem
Existing methods fail to adequately utilize information from multi-view or sequenced images to embed features effectively, particularly in identifying 3D shapes from 2D images, as they do not sufficiently leverage similarity information between sequential inputs.
Innovation Solution
A processor-implemented method that estimates depth maps for multiple 2D images, transforms these into 3D information using camera parameters, and applies this 3D information to a neural network to generate embedded features representing 3D shapes, which can then be used to reconstruct 3D objects from 2D inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If depth maps from multiple 2D images are transformed into 3D information and applied to a neural network, then the embedded feature includes comprehensive 3D shape information, but the device complexity and computational requirements increase
Solution Approach 1:
The patent transforms 2D depth maps into 3D point cloud information, adding a spatial dimension to the feature representation. This dimensional transformation enables the neural network to process and learn 3D shape characteristics from 2D input images, resolving the contradiction by preserving 3D shape information while using a structured transformation approach rather than complex 3D imaging hardware.
Solution Approach 2:
The patent performs preliminary depth estimation on multiple 2D images before feeding them to the neural network. By pre-processing the input data to extract depth information and transform it into 3D point clouds, the system prepares enriched features in advance, reducing the computational burden during network inference while maintaining comprehensive 3D shape information.
2Productivity
If depth maps are incrementally aggregated and transformed into 3D information, then the processing efficiency is improved, but the measurement precision of 3D shape may be compromised
Solution Approach 1:
The patent divides the processing of multiple 2D images into incremental steps, where depth maps from individual images are processed and aggregated sequentially. This segmentation allows efficient batch processing while maintaining the ability to preserve detailed depth information from each view, balancing processing speed with 3D reconstruction accuracy.
Solution Approach 2:
The patent merges depth information from multiple 2D images into a unified 3D point cloud representation. By combining depth maps incrementally and transforming them into 3D space using camera parameters, the system achieves both processing efficiency through structured aggregation and measurement precision through multi-view information fusion.
3Reliability
If 3D information from multiple views is used to generate embedded features, then the reliability of 3D shape identification is improved, but the loss of time for processing increases
Solution Approach 1:
The patent performs preliminary transformations of depth maps into 3D point cloud information before neural network processing. By pre-computing the 3D representation from multiple views and organizing it in an efficient data structure, the system reduces the processing time during actual inference while maintaining high reliability through comprehensive multi-view 3D information.
Solution Approach 2:
The patent creates a 3D point cloud copy of the object from multiple 2D views, which serves as an efficient intermediate representation. This copied 3D structure can be processed more quickly by the neural network compared to raw multi-view images, achieving both time efficiency and reliable 3D shape identification.
Data Source
AI summary
A method and apparatus with feature embedding is provided. The method includes estimating a depth map for each of plural two-dimensional (2D) input images, transforming the depth maps into three-dimensional (3D) information in a point cloud form based on an aggregation of the depth maps, and generating an embedded feature by applying the 3D information to a machine learning model, where the embedded feature includes information about a 3D shape corresponding to a 2D object in the plural 2D input images.