# Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation Source: [Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:08:59.042Z Authors: Jindong Jiang, Zhijun Zhang, Yongqian Huang, Lunan Zheng Published: 2017-05-21T03:02:01.000Z ## Abstract To improve segmentation performance, a novel neural network architecture (termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional neural network (DFCN) with a depth-sensitive fully-connected conditional random field (DCRF). First, a DFCN architecture which fuses depth information into the early layers and applies dilated convolution for later contextual reasoning is designed. Then, a depth-sensitive fully-connected conditional random field (DCRF) is proposed and combined with the previous DFCN to refine the preliminary result. Comparative experiments show that the proposed DFCN-DCRF has the best performance compared with most state-of-the-art methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on SUN RGB-D (test)](): Semantic segmentation; SUN RGB-D (test); mIoU: 39.3. ## Original sources and implementation [Original paper]() [PDF]()