Accelerate 3D Object Processing via Spectral Layout
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 25 Oct 2021.
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9 of 17 references.
References · 9
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
- 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
- 2016Volumetric and Multi-View CNNs for Object Classification on 3D Datacited by 240
- 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
- 2019Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objectscited by 44
- 2016FusionNet: 3D Object Classification Using Multiple Data Representationscited by 12
- 2019A survey of Object Classification and Detection based on 2D/3D datacited by 5
- 2018Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Methodcited by 3
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