CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-Scale Indoor Scene

Hao-Xiang Chen; Jiahui Huang; Tai-Jiang Mu; Shi-Min Hu

ECCV 2022poster10 citations

CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-Scale Indoor Scene

Hao-Xiang Chen, Jiahui Huang, Tai-Jiang Mu, Shi-Min Hu

Abstract

"We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions. It is based on an end-to-end sparse convolutional network, CircNet, which jointly models local geometric details and global scene structural contexts, allowing it to preserve fine-grained object detail while recovering missing regions commonly arising in traditional 3D scene data. A novel differentiable rendering module further enables a test-time refinement for better reconstruction quality. Extensive experiments on both real-world and synthetic datasets show that our concise framework is effective, achieving better reconstruction quality while being significantly faster."

BibTeX

@inproceedings{eccv2022_circleconvolutio,
  title = {CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-Scale Indoor Scene},
  author = {Hao-Xiang Chen and Jiahui Huang and Tai-Jiang Mu and Shi-Min Hu},
  booktitle = {ECCV 2022},
  year = {2022}
}