Joint Depth and Semantic Inference from a Single Image via Elastic Conditional Random Field

作者：

Highlights：

• Efficient joint inference of depth estimation and region labeling from a single image.

• Our first contribution is an efficient generative model called Elastic Conditional Random Field (E-CRF) to capture the interdependency between depth and labeling, along with the spatial dependency among neighborhood superpixels.

• Our second contribution is to further accelerate the above LBP-based generative inference from a large-margin perspective by using a Structured Support Vector Machine (SSVM).

摘要

Highlights•Efficient joint inference of depth estimation and region labeling from a single image.•Our first contribution is an efficient generative model called Elastic Conditional Random Field (E-CRF) to capture the interdependency between depth and labeling, along with the spatial dependency among neighborhood superpixels.•Our second contribution is to further accelerate the above LBP-based generative inference from a large-margin perspective by using a Structured Support Vector Machine (SSVM).

论文关键词：Depth estimation,Semantic labeling,Conditional random field,Structured Support Vector Machine,Content analysis,Scene understanding

论文评审过程：Received 28 July 2015, Revised 20 February 2016, Accepted 9 March 2016, Available online 26 May 2016, Version of Record 23 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.03.016