Neural Network for Point Cloud Detail Inference
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Solution Overview
Problem
Existing methods for enhancing point cloud representations, such as increasing point size or using deep learning, either introduce artifacts or result in slow inference, making them unsuitable for interactive scenarios and lacking in effectively inferring missing image details.
Innovation Solution
A computer-implemented method for forming a dataset configured for learning a neural network architecture that infers missing image details by obtaining a 3D mesh scene, computing point cloud representations, generating camera views, and creating pairs of training samples from rendered viewpoints of both point cloud and 3D mesh scenes, which are then used to train the neural network to improve the inference of missing details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the point size is increased to get a denser representation of the scene, then the recognition of elements is improved, but artifacts are introduced that deform the shape
Solution Approach 1:
The patent uses a neural network to learn from synthetic training data where complete 3D mesh renderings are copied and processed to generate training pairs. The network learns to infer missing details by comparing point cloud renderings with their corresponding complete mesh renderings, allowing it to fill in gaps without introducing artifacts.
Solution Approach 2:
The patent replaces traditional mechanical post-processing methods (like simple point size adjustment) with a data-driven neural network approach. The neural network processes point cloud data through multiple layers, substituting manual geometric operations with intelligent inference based on learned patterns from training data.
2Manufacturing precision
If deep learning approaches are used for enhancing point-clouds, then the completeness of representation is improved, but the inference speed becomes slow
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network on extensive synthetic training data generated from 3D mesh scenes. This pre-training phase allows the network to learn the mapping between point cloud representations and complete scene details, so that during actual inference, the network can quickly apply these learned patterns without requiring slow real-time computation.
Solution Approach 2:
The patent optimizes the neural network architecture parameters and training configurations to achieve efficient inference. By carefully selecting network depth, width, and other hyperparameters, the system achieves fast inference speeds while maintaining high representation completeness.
3Reliability
If point cloud data is obtained from scans, then the representation of real world scenes is achieved, but the representation is sparse and lacks information
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously refines the point cloud representation by comparing it with the original 3D mesh ground truth during training. The network learns to predict missing information by analyzing the relationship between sparse point cloud data and complete mesh representations, effectively filling in gaps and improving information completeness while maintaining fidelity to the original scene.
Data Source
AI summary
This disclosure notably relates to a computer-implemented method for forming a dataset configured for learning a neural network architecture configured for inferring missing image details of a point cloud rendering. The method comprises the steps of obtaining a 3D mesh scene, computing a point cloud representation of the 3D mesh scene, generating one or more camera views of the 3D mesh scene and the point cloud representation. For each camera view, the method renders a viewpoint of the point cloud representation, of the 3D mesh scene, computes another point cloud representation of the viewpoint of the 3D mesh scene, and renders a viewpoint of the other point cloud representation. The method also comprises obtaining a pair of training samples, each comprising respectively the rendered viewpoint of the point cloud representation and the rendered viewpoint of the other point cloud representation; and adding the pair of training samples to the dataset.