3D Triplane Reconstruction for High-Resolution, Lower-Cost Processing
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Solution Overview
Problem
Existing 3D reconstruction methods, such as FineRecon, are computationally expensive and memory-intensive due to processing large 3D volumes, limiting the resolution and efficiency of 3D reconstructions.
Innovation Solution
The use of 3D triplane representation, projecting a 3D volume onto three orthogonal planes (XY, XZ, YZ) and processing with separate 2D convolutions to generate a 3D representation by combining triplane and image features, reducing computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D convolutions are used for processing 3D volumes in 3D reconstruction, then the reconstruction accuracy can be maintained, but the computational cost and memory requirements increase significantly
Solution Approach 1:
The patent segments the 3D volume processing into three separate 2D planes (XY, XZ, YZ), processing each plane independently with 2D convolutions. This segmentation reduces the computational complexity from O(n³) for 3D convolutions to O(3n²) for 2D convolutions, significantly lowering computational cost while preserving reconstruction accuracy through the combination of triplane features
Solution Approach 2:
The patent transforms the problem from 3D space to 2D space by projecting the 3D volume onto three orthogonal 2D planes. This dimensionality reduction allows the use of computationally efficient 2D convolutions instead of expensive 3D convolutions, while the triplane representation maintains the 3D structural information needed for accurate reconstruction
2Measurement precision
If high resolution 3D volumes are processed, then the reconstruction quality improves, but the memory requirements become prohibitive
Solution Approach 1:
The patent divides the large 3D volume into three separate 2D planes, each with significantly smaller memory footprint. Processing three 2D planes requires far less memory than processing a single high-resolution 3D volume, enabling high-resolution reconstruction without prohibitive memory requirements
Solution Approach 2:
By projecting the 3D volume onto 2D planes, the patent reduces the memory complexity from O(n³) to O(3n²). This dimensionality change allows high-resolution feature extraction in 2D space while maintaining the ability to reconstruct 3D structures, effectively reducing memory requirements for high-resolution processing
3Measurement precision
If 3D volumes are processed at high resolution, then the spatial resolution of reconstruction improves, but the processing time increases
Solution Approach 1:
The patent segments the processing into three independent 2D plane processing tasks that can be executed in parallel. This segmentation, combined with the efficiency of 2D convolutions, reduces the overall processing time compared to sequential 3D convolution processing, while maintaining high spatial resolution through the triplane feature combination
Solution Approach 2:
The patent performs high-resolution feature extraction in 2D space where computations are faster, then combines the results to achieve high spatial resolution 3D reconstruction. This approach avoids the computational burden of high-resolution 3D convolutions while preserving the spatial resolution benefits through the triplane representation
Data Source
AI summary
An apparatus for generation of a 3D representation of a scene includes a memory for storing a plurality of images depicting a scene; and processing circuitry in communication with the memory. The processing circuitry is configured to generate a depth map for the plurality of input images depicting a scene and unproject the depth map to a three-dimensional (3D) point cloud. The processing circuitry is also configured to project a plurality of 3D points from the 3D point cloud to a plurality of two dimensional (2D) planes and extract a plurality of triplane features from the plurality of 2D planes. The processing circuitry is further configured to generate a 3D representation of the scene based on combining the plurality of triplane features and a plurality of image features extracted from the plurality of input images.