3D Mesh Generation From Volume Decoding Without Floater Artifacts
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Solution Overview
Problem
Conventional mesh generation techniques result in inaccurate meshes due to the incorporation of artifacts like 'floaters' and are computationally inefficient, leading to visual inaccuracies and increased power consumption.
Innovation Solution
A mesh generation system that utilizes a transformer model to create a triplane Neural Radiance Field (NeRF) from digital images, decoding density information using differentiable marching cubes and differentiable rasterization to generate a mesh, excluding artifacts with zero density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional mesh generation techniques are used, then mesh generation can be performed, but the meshes contain artifacts like floaters and are visually inaccurate
Solution Approach 1:
The patent extracts and removes harmful artifacts (floaters) from the mesh generation process by using density-based filtering. The Marching Cubes algorithm processes a density field where artifacts have zero density, allowing them to be naturally excluded from the final mesh while preserving valid geometric structures.
Solution Approach 2:
The patent changes the representation parameter from traditional point-cloud or polygon-based methods to a density field representation. This parameter transformation enables continuous differentiation and thresholding, allowing precise separation of valid surfaces from artifacts based on density values.
2Productivity
If conventional mesh generation techniques are used, then meshes can be generated, but computational efficiency is poor and power consumption increases
Solution Approach 1:
The patent replaces traditional mechanical/computational geometry algorithms with a neural network-based approach. The neural network directly regresses density fields from input data, eliminating the need for complex iterative optimization and geometric processing that characterize conventional methods, thereby reducing computational time and energy consumption.
Solution Approach 2:
By transforming the problem into a density field regression task using neural networks, the patent changes the computational approach from discrete geometric processing to continuous field-based computation, which is more efficient and scalable.
3Manufacturing precision
If more data is used in mesh generation, then mesh representation can be improved, but errors and visual inaccuracies increase
Solution Approach 1:
The patent changes the data representation parameter to a density field, which provides continuous and differentiable information about geometric structures. This parameter transformation enables the system to distinguish between valid surfaces and artifacts based on density thresholds, improving visual accuracy even when processing extensive input data.
Solution Approach 2:
The neural network-based approach incorporates implicit feedback through density field optimization, where the model learns to distinguish valid geometric structures from artifacts through training on labeled data, automatically adjusting its representations to maximize visual accuracy.
Data Source
AI summary
In implementation of techniques for generating meshes by decoding volume representations, a computing device implements a mesh generation system to receive digital images depicting an object from different angles. The mesh generation system generates a volume representation of the object using a transformer model based on the digital images. By decoding information from the volume representation using an algorithm, the mesh generation system then generates a mesh of the object from the volume representation. The mesh generation system then presents the mesh of the object in a user interface.