3D Object Reconstruction Using Semantic Key Point Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional three-dimensional object reconstruction methods require extensive computational resources and suffer from low accuracy due to the need for numerous iterative computations to determine feature point correspondences in video frames.
Innovation Solution
Determine key point information using color and depth video subframes, leveraging deep neural networks to identify key points with two-dimensional and three-dimensional location information, and utilize physical meaning information to establish correspondences between frames, thereby facilitating efficient and accurate point cloud data registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional point cloud data registration methods are used, then feature point correspondences can be determined, but the computational burden is high and processing time is long
Solution Approach 1:
The patent performs preliminary action by pre-establishing correspondence relationships between key points in different video frames using deep neural networks before actual point cloud registration. The system extracts key point information including physical meaning information in advance, creating a lookup table of correspondences that can be quickly applied during registration, avoiding the need for time-consuming iterative computations at runtime.
Solution Approach 2:
The patent replaces the mechanical iterative computation system with a neural network-based recognition system. Instead of using traditional ICP (Iterative Closest Point) algorithms that require repeated mathematical computations to find correspondences, the system uses deep neural networks to directly identify and match key points based on their physical meaning information, significantly reducing computational complexity and processing time.
2Measurement precision
If conventional point cloud data registration methods are used, then feature point correspondences can be determined, but the computational resources required are extensive
Solution Approach 1:
The patent extracts only the essential key point information needed for registration from the full point cloud data. By identifying and extracting key points with specific physical meaning information (such as facial landmarks in face reconstruction) rather than processing all surface points, the system significantly reduces the data volume and computational resources required while maintaining registration accuracy.
Solution Approach 2:
The patent changes the parameter representation from raw three-dimensional coordinates to a more efficient feature space defined by physical meaning information. This parameter transformation allows the neural network to work with semantically meaningful features rather than raw geometric data, reducing the dimensionality and complexity of computations while preserving the essential information needed for accurate correspondence determination.
3Productivity
If key point information with physical meaning is used, then correspondence determination becomes more efficient, but the complexity of key point extraction increases
Solution Approach 1:
The patent introduces an intermediary component - the deep neural network - that bridges the gap between raw video frame data and meaningful key point correspondences. The neural network acts as a mediator that automatically learns and extracts physical meaning information from unstructured data, transforming it into structured correspondence relationships without requiring manual feature engineering or complex extraction algorithms.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically learn and determine key point correspondences without human intervention or pre-programmed rules. The deep neural network is trained to autonomously identify physical meaning information and establish correspondences between frames, making the complex extraction process self-contained and eliminating the need for external expertise or manual configuration.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
Embodiments of the present application disclose a three-dimensional object reconstruction method, comprising: acquiring multiple video frames of an object to be constructed, any of the video frames comprising a color video sub-frame and a depth video sub-frame; determining, according to the color video sub-frame, key point information of the object in the video frame, the key point information comprising two-dimensional position information of a key point and physical meaning information of the key point with respect to the object, wherein the physical meaning information represents, for any video frame, a position of the object which the key point represents, such that a matching relationship of the key point between the multiple video frames is determinable on the basis of the physical meaning information of the key point with respect to the object, so as to indicate which key point has the same physical meaning information in the multiple video frames; and performing efficient alignment on point cloud data in the multiple video frames according to the matching relationship and three-dimensional position information of the key point. The invention reduces the amount of computation and achieves high accuracy, thereby providing a superior reconstruction of three-dimensional objects.