normal field octree input (d-depth) ... convolution pooling convolution pooling d d-1 3 2 ... ... ... Fig. 1 . An illustration of our octree-based convolutional neural network (O-CNN). Our method represents the input shape with an octree and feeds the averaged normal vectors stored in the finest leaf octants to the CNN as input. All the CNN operations are efficiently executed on the GPU and the resulting features are stored in the octree structure. Numbers inside the blue dashed square denote the depth of the octants involved in computation. We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis. Built upon the octree representation of 3D shapes, our method takes the average normal vectors of a 3D model sampled in the finest leaf octants as input and performs 3D CNN operations on the octants occupied by the 3D shape surface. We design a novel octree data structure to efficiently store the octant information and CNN features into the graphics memory and execute the entire O-CNN training and evaluation on the GPU. O-CNN supports various CNN structures and works for 3D shapes in different representations. By restraining the computations on the octants occupied by 3D surfaces, the memory and computational costs of the O-CNN grow quadratically as the depth of the octree increases, which makes the 3D CNN feasible for high-resolution 3D models. We compare the performance of the O-CNN with other existing 3D CNN solutions and demonstrate the efficiency and efficacy of O-CNN in three shape analysis tasks, including object classification, shape retrieval, and shape segmentation.
REF ) performs 3D CNN on the octree representation of 3D shapes, showing more compact storage and fast computation than existing model-based approaches.
O-CNN: octree-based convolutional neural networks for 3D shape analysis
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Computer Science