# Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks Source: [Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:24:19.140Z Authors: Jinghua Wang, Zhenhua Wang, Dacheng Tao, Simon See, Gang Wang Published: 2016-08-03T06:05:16.000Z ## Abstract In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We take advantage of deconvolutional networks which can predict pixel-wise class labels, and develop a new structure for deconvolution of multiple modalities. We propose a novel feature transformation network to bridge the convolutional networks and deconvolutional networks. In the feature transformation network, we correlate the two modalities by discovering common features between them, as well as characterize each modality by discovering modality specific features. With the common features, we not only closely correlate the two modalities, but also allow them to borrow features from each other to enhance the representation of shared information. With specific features, we capture the visual patterns that are only visible in one modality. The proposed network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2. ## 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 NYU v2 (test)](): Semantic segmentation; NYU v2 (test); mIoU: not recorded. ## Original sources and implementation [Original paper]() [PDF]()