3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks

22 Nov 2017  ·  Yizhak Ben-Shabat, Michael Lindenbaum, Anath Fischer ·

The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods. The common solution of transforming the data into a 3D voxel grid introduces its own challenges, mainly large memory size. In this paper we propose a novel 3D point cloud representation called 3D Modified Fisher Vectors (3DmFV). Our representation is hybrid as it combines the discrete structure of a grid with continuous generalization of Fisher vectors, in a compact and computationally efficient way. Using the grid enables us to design a new CNN architecture for point cloud classification and part segmentation. In a series of experiments we demonstrate competitive performance or even better than state-of-the-art on challenging benchmark datasets.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
3D Point Cloud Classification ModelNet40 3DMFV-Net Overall Accuracy 91.6 # 87
3D Part Segmentation ShapeNet-Part 3DmFV-Net Instance Average IoU 84.3 # 57

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