3D Point Cloud Upsampling via Deep Learning Reference
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Solution Overview
Problem
Conventional methods for generating dense 3D point clouds using high-performance hardware are costly, and the resulting point clouds often contain noise, leading to inaccurate 3D reconstructions in virtual reality applications.
Innovation Solution
A method that uses a low-performance hardware device to sparsely record point clouds and then employs computer vision technology, such as deep learning, to upsample these point clouds, referencing a denser point cloud of a predetermined shape to generate a target object with a fitted curved surface and increased point density, thereby improving 3D reconstruction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-performance hardware devices are used to directly generate dense point clouds, then the 3D representation quality is improved, but the hardware cost increases unduly
Solution Approach 1:
The patent uses inexpensive hardware devices to capture sparse point clouds instead of expensive high-performance hardware. The sparse point clouds are then processed through deep learning algorithms to achieve dense 3D representations, effectively replacing costly hardware with a combination of cheap hardware and computational processing.
Solution Approach 2:
The patent introduces deep learning algorithms as an intermediary between sparse point cloud capture and dense 3D representation. The algorithm processes the sparse data to generate dense point clouds, acting as a mediator that bridges the gap between low-cost capture and high-quality output.
2Manufacturing precision
If high-performance hardware devices are used to generate dense point clouds, then the resolution of 3D representation is improved, but the hardware complexity increases
Solution Approach 1:
The patent employs simple, low-performance hardware devices for point cloud capture rather than complex high-performance hardware. The complexity is shifted from the hardware to the software processing stage, where deep learning algorithms enhance the sparse data to achieve dense representations.
Solution Approach 2:
The patent replaces complex mechanical/hardware systems with computational algorithms. Instead of using sophisticated hardware to directly capture dense point clouds, the system uses simple hardware combined with deep learning algorithms to achieve the same result through data processing.
3Ease of manufacture
If sparse point clouds are recorded by low-performance hardware, then the hardware cost is reduced, but the 3D reconstruction accuracy deteriorates
Solution Approach 1:
The patent introduces deep learning algorithms as an intermediary processing stage that takes sparse point clouds from low-performance hardware and transforms them into dense, accurate 3D representations. This intermediary computation compensates for the limitations of the simple capture hardware.
Solution Approach 2:
The patent changes the density parameter of point clouds through algorithmic processing. The deep learning algorithms transform sparse point clouds (low density) into dense point clouds (high density), effectively changing the data parameters to achieve high reconstruction accuracy despite using simple hardware.
4Device complexity
If sparse point clouds are captured by inexpensive hardware, then the hardware complexity is reduced, but the point cloud density decreases
Solution Approach 1:
The patent applies parameter transformation through deep learning algorithms that increase point cloud density. The algorithms take sparse point clouds (low quantity/density) and generate dense point clouds (high quantity/density), effectively changing the data parameters to achieve high point density despite using simple hardware.
Solution Approach 2:
The patent uses deep learning algorithms as an intermediary that processes and densifies sparse point clouds. This computational intermediary bridges the gap between low-density captured data and high-density required output, multiplying the effective point cloud density through intelligent processing.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for generating a target object. The method includes upsampling a first feature of an input point cloud. The method further includes determining a reference feature of a reference point cloud having a predetermined shape. The method further includes determining a second feature based on the upsampled first feature and the reference feature. The method further includes generating a three-dimensional target object based on the second feature and the input point cloud, wherein the target object has a fitted curved surface, and the target object has a greater number of points than the input point cloud. With embodiments of the present disclosure, a point cloud of the target object can be made denser, and the number of points can be increased, thereby achieving a more accurate three-dimensional reconstruction of the target object.