Planar Gaussian Splatting for Annotation-Free 3D Reconstruction
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Solution Overview
Problem
Existing 3D planar reconstruction methods require 3D geometry or plane annotations and suffer from domain-shift issues when inference images have different properties than training images, leading to performance degradation.
Innovation Solution
Planar Gaussian Splatting, a neural rendering method that learns 3D geometry and parses planar surfaces directly from multiple images without requiring 3D plane annotations, using 2D mask proposals and a hierarchical Gaussian Mixture Model (GMM) to merge similar primitives into scene geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D planar reconstruction methods are used, then accurate 3D geometry can be obtained, but 3D plane annotations are required and domain-shift issues occur when inference images differ from training images
Solution Approach 1:
The patent replaces traditional geometric computer vision methods with a neural rendering approach. Instead of using conventional 3D reconstruction algorithms that rely on hand-crafted geometric constraints and 3D plane annotations, the system employs a neural network to learn 3D geometry and planar surface properties directly from 2D images, substituting mechanical/geometric processing with neural network-based learning
Solution Approach 2:
The patent changes the parameter representation by introducing a hierarchical Gaussian Mixture Model that represents 3D scenes as collections of Gaussian primitives with learnable parameters. This allows the system to represent complex 3D geometries using simple parametric primitives that can be efficiently optimized and generalized across different datasets, avoiding the domain-shift issues of traditional methods
2Measurement precision
If traditional 3D reconstruction methods are used, then accurate scene geometry can be obtained, but processing time is excessive
Solution Approach 1:
The patent replaces computationally intensive traditional 3D reconstruction algorithms with a neural rendering pipeline that leverages differentiable rendering and gradient-based optimization. This substitution enables faster processing by utilizing modern GPU acceleration and efficient neural network computations while maintaining geometric accuracy
Solution Approach 2:
The patent performs preliminary actions by pre-defining a hierarchical structure of Gaussian primitives and establishing the rendering pipeline before optimization begins. The system pre-processes images to extract initial features and organizes the 3D scene representation in advance, which accelerates the subsequent optimization process compared to traditional methods that compute geometry on-the-fly
Data Source
AI summary
Techniques are described for image processing. For example, a computing device can segment, using a first neural network, image(s) of a scene to determine respective segments for each of the image(s). The computing device can determine, using a second neural network, normal vectors for each of the image(s). The computing device can generate a graph based on each respective segment for each image, each respective normal vectors for each image, and estimated planar distances. The computing device can partition, based on the normal vectors and the estimated planar distances, the graph to determine indexes associated with Gaussian primitives. The computing device can assign, using linear regression, each descriptor of a plurality of descriptors to an index of the plurality of indexes based on a respective weight. The computing device can merge, using a Gaussian tree, Gaussian primitives of the Gaussian primitives with associated indexes that are similar to each other.