# DeOccNet: Learning to See Through Foreground Occlusions in Light Fields

arXiv: https://arxiv.org/abs/1912.04459 · HTML: https://mlsanity.com/paper/1912.04459/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Light Field Image Super-Resolution with Transformers](https://mlsanity.com/paper/2108.07597/) (2021)
- [Event-based Synthetic Aperture Imaging with a Hybrid Network](https://mlsanity.com/paper/2103.02376/) (2021)
- [LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images](https://mlsanity.com/paper/2209.02197/) (2022)
- [Dense Dual-Attention Network for Light Field Image Super-Resolution](https://mlsanity.com/paper/2110.12114/) (2021)
- [Learning to See Through with Events](https://mlsanity.com/paper/2212.02219/) (2022)
- [Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views](https://mlsanity.com/paper/2307.00821/) (2023)
- [Toward Real-World Light Field Super-Resolution](https://mlsanity.com/paper/2305.18994/) (2023)
- [Seeing Behind Dynamic Occlusions with Event Cameras](https://mlsanity.com/paper/2307.15829/) (2023)
- [I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images](https://mlsanity.com/paper/2301.06392/) (2023)
- [Vision-Reasoning-Guided Occlusion Removal from Light Fields](https://mlsanity.com/paper/2606.19985/) (2026)

## Abstract (arXiv)

Background objects occluded in some views of a light field (LF) camera can be seen by other views. Consequently, occluded surfaces are possible to be reconstructed from LF images. In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet). In our method, sub-aperture images (SAIs) are first given to the encoder to incorporate both spatial and angular information. The encoded representations are then used by the decoder to render an occlusionfree center-view SAI. To the best of our knowledge, DeOccNet is the first deep learning-based LF-DeOcc method. To handle the insufficiency of training data, we propose an LF synthesis approach to embed selected occlusion masks into existing LF images. Besides, several synthetic and realworld LFs are developed for performance evaluation. Experimental results show that, after training on the generated data, our DeOccNet can effectively remove foreground occlusions and achieves superior performance as compared to other state-of-the-art methods. Source codes are available at: https://github.com/YingqianWang/DeOccNet.