Accelerate 3D Object Processing via Spectral Layout
This addresses the computational bottleneck in 3D computer vision, though it appears incremental as it adapts existing spectral techniques to a new domain.
The paper tackles the high computational cost of 3D image processing by embedding 3D objects into 2D space using spectral layout, enabling the use of 2D-based methods and demonstrating effectiveness and efficiency in experiments.
3D image processing is an important problem in computer vision and pattern recognition fields. Compared with 2D image processing, its computation difficulty and cost are much higher due to the extra dimension. To fundamentally address this problem, we propose to embed the essential information in a 3D object into 2D space via spectral layout. Specifically, we construct a 3D adjacency graph to capture spatial structure of the 3D voxel grid. Then we calculate the eigenvectors corresponding to the second and third smallest eigenvalues of its graph Laplacian and perform spectral layout to map each voxel into a pixel in 2D Cartesian coordinate plane. The proposed method can achieve high quality 2D representations for 3D objects, which enables to use 2D-based methods to process 3D objects. The experimental results demonstrate the effectiveness and efficiency of our method.