mlsanityData to 13 Oct 2026

Multi-modal Bifurcated Network for Depth Guided Image Relighting

arXiv 2105.00690 ↗First posted 3 May 2021cs.CVcited by 6

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Abstract (arXiv)

Image relighting aims to recalibrate the illumination setting in an image. In this paper, we propose a deep learning-based method called multi-modal bifurcated network (MBNet) for depth guided image relighting. That is, given an image and the corresponding depth maps, a new image with the given illuminant angle and color temperature is generated by our network. This model extracts the image and the depth features by the bifurcated network in the encoder. To use the two features effectively, we adopt the dynamic dilated pyramid modules in the decoder. Moreover, to increase the variety of training data, we propose a novel data process pipeline to increase the number of the training data. Experiments conducted on the VIDIT dataset show that the proposed solution obtains the 1st place in terms of SSIM and PMS in the NTIRE 2021 Depth Guide One-to-one Relighting Challenge.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 3 May 2021.

Builds on

18 of 33 references.

References · 18

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6 more
  1. 2020VIDIT: Virtual Image Dataset for Illumination Transfercited by 26
  2. 2021NTIRE 2021 Depth Guided Image Relighting Challengecited by 23
  3. 2020AIM 2020: Scene Relighting and Illumination Estimation Challengecited by 18
  4. 2020Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazingcited by 14
  5. 2020WDRN : A Wavelet Decomposed RelightNet for Image Relightingcited by 14
  6. 2020Wavelet Channel Attention Module with a Fusion Network for Single Image Derainingcited by 7

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

6 papers.