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Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
19thMARCH2017

READING

J. Huang, S. You. Point cloud labeling using 3d convolutional neural network. ICPR, 2016.

Huang and You use simple 3D convolutional networks for point cloud labeling. Given a big point cloud, e.g. consisting of a part of Ottawa, they extract individual point clouds by moving a center point through the point cloud and extracting a cubic bounding box with defined radius. The extracted point cloud is transformed to a voxelized occupancy grid used as input. The labels are inferred using a voting scheme for each voxel (as multiple labels can be present in each voxel). They claim to use $8000$ cells as input, which would correspond to $20 \times 20 \times 20$. This is, indeed, rather small, as they claim that 3D convolutional networks quickly reach the memory limit.

The used network is rather simple and supposed to perform per-pixel semantic segmentation. Motivated by LeNet [1], the network consists of two 3D convolutional layers (where the convolutional layer is extended to 3D in a straight-forward way) and two 3D pooling layers, followed by a fully connected layer. This is illustrated in Figure 1. They present qualitative results in Figure 2.

Figure 1 (click to enlarge): Illustration of the used network architecture.

Figure 2: Qualitative results.

  • [1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or using the following platforms: