Augmented Point Cloud via Deep Learning for Missing Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current point cloud visualization systems face challenges in capturing complete scene geometry due to occlusions and missing data points, leading to incomplete or coarse point clouds that result in low-resolution images, especially when objects are partially hidden or when depth information is lacking.
Innovation Solution
A deep learning-based approach that learns patterns from surrounding data points to fill or replace missing data in the point cloud, using multiple layers of representation to incorporate shape, color, and contextual information, allowing for the addition of detailed features like patterns on occluded objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is captured using conventional imaging methods, then the system is simple and fast to capture, but the point cloud is incomplete and low-resolution due to occlusions and missing data
Solution Approach 1:
The patent introduces an intermediary deep learning model that acts as a mediator between the incomplete captured point cloud and the complete 3D scene representation. This model fills missing data points by learning patterns from surrounding points and generating plausible completions, effectively mediating the information gap caused by occlusions and limited viewing angles.
Solution Approach 2:
The patent applies preliminary action by pre-training deep learning models on large datasets of complete 3D objects and their point cloud representations. This preliminary learning enables the model to predict and fill missing points in new, incomplete point clouds without requiring additional capture attempts or manual intervention.
2Loss of information
If multiple cameras are used to capture images from different angles, then more complete scene geometry can be captured, but the system complexity and cost increase
Solution Approach 1:
The patent uses copying by creating virtual views of the scene through deep learning-based point cloud completion. Instead of physically capturing the scene from multiple angles with multiple cameras, the system learns the complete 3D structure from a single viewpoint and generates synthetic point clouds representing other perspectives, effectively copying the information that would otherwise require multiple physical cameras.
3Measurement precision
If point cloud resolution is increased by capturing more data points, then image quality improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent applies parameter changes by transforming the point cloud data through learned transformations in the deep neural network. The model learns optimal parameter representations of 3D structures that enable efficient completion of missing points, achieving high-resolution output without proportionally increasing input data volume or processing time.
Data Source
AI summary
A visualization system that uses deep learning can store data representing a scene in multiple layers of representation. Each layer can include a different category of data from the other layers. The categories of data can increase in complexity and specificity from a lowermost layer to an uppermost layer. For instance, first, second, and third layers can store information corresponding to edges, corners, and surface finishes present in the scene, respectively. The visualization system can retrieve data representing the scene from multiple layers, and augment a point cloud representation of the scene in response to the retrieved data. The point cloud can be augmented to increase a point density in one or more point cloud regions that lack data or include only sparse data. Downstream, the visualization system can use the augmented point cloud to create improved images of the scene from desired points of view.