mlsanityData to 14 Oct 2026

Cutting-Edge Techniques for Depth Map Super-Resolution

arXiv 2306.15244 ↗First posted 27 Jun 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

To overcome hardware limitations in commercially available depth sensors which result in low-resolution depth maps, depth map super-resolution (DMSR) is a practical and valuable computer vision task. DMSR requires upscaling a low-resolution (LR) depth map into a high-resolution (HR) space. Joint image filtering for DMSR has been applied using spatially-invariant and spatially-variant convolutional neural network (CNN) approaches. In this project, we propose a novel joint image filtering DMSR algorithm using a Swin transformer architecture. Furthermore, we introduce a Nonlinear Activation Free (NAF) network based on a conventional CNN model used in cutting-edge image restoration applications and compare the performance of the techniques. The proposed algorithms are validated through numerical studies and visual examples demonstrating improvements to state-of-the-art performance while maintaining competitive computation time for noisy depth map super-resolution.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 27 Jun 2023.

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14 of 21 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
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  10. 2015The Fast Bilateral Solvercited by 87
  11. 2021Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baselinecited by 61
  12. 2019Deformable Kernel Networks for Joint Image Filteringcited by 59
2 more
  1. 2021High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusioncited by 20
  2. 2023A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolutioncited by 5

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