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.
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Paper [PDF]
Slides [PPTX]
Code [Github]
Citation [BibTeX]
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong.
2017. O-CNN: Octree-based Convolutional Neural Networks for 3D Shape
Analysis. ACM Trans. Graph. (SIGGRAPH) 36, 4, Article 72 (July 2017), 11 pages.
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