Weighted Voxel

TL;DR: Weighted Voxel replaces the zero-one occupancy grid with a richer voxel representation that retains structural information, improving reconstruction quality while taking less time to train.

Abstract

3D reconstruction has been attracting increasing attention in the past few years. With the surge of deep neural networks, the performance of 3D reconstruction has been improved significantly. However, the voxel reconstructed by extant approaches usually contains lots of noise and leads to heavy computation. In this paper, we define a new voxel representation, named Weighted Voxel. It provides more abundant information, facilitating the subsequent learning and generalization steps. Unlike regular voxel which consists of zero-one, the proposed Weighted Voxel makes full use of the structure information of voxels. Experimental results demonstrate that Weighted Voxel not only performs better in reconstruction but also takes less time in training.

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BibTeX Copy

@inproceedings{xie2018weighted,
  title={Weighted Voxel: a novel voxel representation for 3D reconstruction},
  author={Xie, Haozhe and
          Yao, Hongxun and
          Sun, Xiaoshuai and
          Zhou, Shangchen and
          Tong, Xiaojun},
  booktitle={International Conference on Internet Multimedia Computing and Service (ICIMCS)},
  year={2018}
}