# Depth-Adapted CNNs for RGB-D Semantic Segmentation Source: [Depth-Adapted CNNs for RGB-D Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:15:16.518Z Authors: Zongwei Wu, Guillaume Allibert, Christophe Stolz, Chao Ma, C\\'edric Demonceaux Published: 2022-06-08T14:59:40.000Z ## Abstract Recent RGB-D semantic segmentation has motivated research interest thanks to the accessibility of complementary modalities from the input side. Existing works often adopt a two-stream architecture that processes photometric and geometric information in parallel, with few methods explicitly leveraging the contribution of depth cues to adjust the sampling position on RGB images. In this paper, we propose a novel framework to incorporate the depth information in the RGB convolutional neural network (CNN), termed Z-ACN (Depth-Adapted CNN). Specifically, our Z-ACN generates a 2D depth-adapted offset which is fully constrained by low-level features to guide the feature extraction on RGB images. With the generated offset, we introduce two intuitive and effective operations to replace basic CNN operators: depth-adapted convolution and depth-adapted average pooling. Extensive experiments on both indoor and outdoor semantic segmentation tasks demonstrate the effectiveness of our approach. ## 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 KITTI (test)](): Semantic segmentation; KITTI (test); mIoU: 35.8. - [Semantic Segmentation on NYU v2 (test)](): Semantic segmentation; NYU v2 (test); mIoU: 51.2. ## Original sources and implementation [Original paper]() [PDF]()