{
  "id": "1809.07917",
  "version": "v1",
  "published": "2018-09-21T02:24:48.000Z",
  "updated": "2018-09-21T02:24:48.000Z",
  "title": "Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes",
  "authors": [
    "Peng-Shuai Wang",
    "Chun-Yu Sun",
    "Yang Liu",
    "Xin Tong"
  ],
  "journal": "ACM Transactions on Graphics, 2018",
  "categories": [
    "cs.CV",
    "cs.GR"
  ],
  "abstract": "We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based CNN methods that represent a 3D shape with voxels in the same resolution, our method represents a 3D shape adaptively with octants at different levels and models the 3D shape within each octant with a planar patch. Based on this adaptive patch-based representation, we propose an Adaptive O-CNN encoder and decoder for encoding and decoding 3D shapes. The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant. As a general framework for 3D shape analysis and generation, the Adaptive O-CNN not only reduces the memory and computational cost, but also offers better shape generation capability than the existing 3D-CNN approaches. We validate Adaptive O-CNN in terms of efficiency and effectiveness on different shape analysis and generation tasks, including shape classification, 3D autoencoding, shape prediction from a single image, and shape completion for noisy and incomplete point clouds.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-09-21T02:24:48.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "3d shape",
      "patch-based deep representation",
      "octree-based convolutional neural network",
      "offers better shape generation capability",
      "adaptive o-cnn encoder"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "ACM",
      "journal": "ACM Trans. Graphics"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}