# Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks Source: [Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:13:37.567Z Authors: Sami Barchid, Jos\\'e Mennesson, Chaabane Dj\\'eraba Published: 2021-05-25T13:30:19.000Z ## Abstract Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works. ## 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. ## Original sources and implementation [Original paper]() [PDF]()