mlsanityData to 7 Oct 2026

Depth-Adapted CNNs for RGB-D Semantic Segmentation

arXiv 2206.03939 ↗First posted 8 Jun 2022cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 63 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

40 of 63 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 0; 2026: 1.

6 papers.

  1. 2023Segment Anything in 3D with Radiance Fieldscited by 61
  2. 2023DFormer: Rethinking RGBD Representation Learning for Semantic Segmentationcited by 58
  3. 2023Pixel Difference Convolutional Network for RGB-D Semantic Segmentationcited by 13
  4. 2022Robust RGB-D Fusion for Saliency Detectioncited by 12
  5. 2024Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applicationscited by 0
  6. 2026Efficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidancecited by 0