Neural Point Cloud Generation With Denoising and 2D Rendering

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Solution Overview

Problem

Existing techniques for generating point clouds require significant computing resources and can be improved for efficiency.

Innovation Solution

A neural network-based approach that includes dataset preparation, noise addition, and denoising processes to generate high-quality 3D point clouds, utilizing a two-pass point splatter method for rendering and a convolutional neural network to predict and remove noise from point clouds.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional techniques are used to generate point clouds, then point cloud generation can be achieved, but significant computing resources are required

Engineering Contradiction:

Improvepoint cloud generation efficiencyVSAvoidcomputing resources

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent creates a neural network model that learns to generate point clouds by copying patterns from 2D image data. The network is trained to transform 2D images into 3D point clouds, effectively copying the spatial relationships and structural information from the image domain to the point cloud domain, thereby reducing the need for computationally intensive traditional 3D reconstruction methods

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces traditional mechanical/computational geometry methods with a neural network-based approach. Instead of using complex algorithms to manually compute 3D structures from images, the system uses a learned neural network model that automatically transforms 2D image data into 3D point cloud representations, substituting computational mechanics with data-driven learning

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Use of energy by moving object

If neural network-based approach is used to generate point clouds, then computational demands are reduced, but the quality of generated point clouds may be compromised

Engineering Contradiction:

Improvecomputational demandsVSAvoidpoint cloud quality

Core Design Contradiction:

Use of energy by moving objectVSManufacturing precision

Solution Approach 1:

The patent performs preliminary training of the neural network on a comprehensive dataset of 2D images and their corresponding 3D point cloud labels. This preliminary action establishes the network's ability to accurately transform images to point clouds before actual use, ensuring high generation quality is pre-achieved through learning from diverse examples

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent incorporates feedback mechanisms during training where the neural network's predictions are compared against ground truth point cloud labels, and the network parameters are adjusted based on the error. This feedback loop ensures the network continuously refines its ability to generate high-quality point clouds that accurately represent the input images

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12555260B1Neural network-based point cloud generation

Publication Date: 2026.02.17 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to identify a three-dimensional (3D) point cloud. In at least one embodiment, one or more three-dimensional (3D) point clouds of one or more objects based is generated using one or more neural networks based, at least in part, on one or more two-dimensional (2D) images and the one or more 3D point clouds.