# Pupil-Adaptive 3D Holography Beyond Coherent Depth-of-Field

arXiv: https://arxiv.org/abs/2409.00028 · HTML: https://mlsanity.com/paper/2409.00028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects](https://mlsanity.com/paper/1904.03167/) (2019)
- [Spatially-Adaptive Pixelwise Networks for Fast Image Translation](https://mlsanity.com/paper/2012.02992/) (2020)
- [Decouple Learning for Parameterized Image Operators](https://mlsanity.com/paper/1807.08186/) (2018)
- [Gaze-Contingent Retinal Speckle Suppression for Perceptually-Matched Foveated Holographic Displays](https://mlsanity.com/paper/2108.06192/) (2021)
- [An Efficient Accelerator Design Methodology for Deformable Convolutional Networks](https://mlsanity.com/paper/2006.05238/) (2020)
- [High-contrast, speckle-free, true 3D holography via binary CGH optimization](https://mlsanity.com/paper/2201.02619/) (2022)

## Built on by (most cited first, 2 of 2)

- [Complex-Valued Holographic Radiance Fields](https://mlsanity.com/paper/2506.08350/) (2025)
- [Eyepiece-free pupil-optimized holographic near-eye displays](https://mlsanity.com/paper/2507.22420/) (2025)

## Abstract (arXiv)

Recent holographic display approaches propelled by deep learning have shown remarkable success in enabling high-fidelity holographic projections. However, these displays have still not been able to demonstrate realistic focus cues, and a major gap still remains between the defocus effects possible with a coherent light-based holographic display and those exhibited by incoherent light in the real world. Moreover, existing methods have not considered the effects of the observer's eye pupil size variations on the perceived quality of 3D projections, especially on the defocus blur due to varying depth-of-field of the eye. In this work, we propose a framework that bridges the gap between the coherent depth-of-field of holographic displays and what is seen in the real world due to incoherent light. To this end, we investigate the effect of varying shape and motion of the eye pupil on the quality of holographic projections, and devise a method that changes the depth-of-the-field of holographic projections dynamically in a pupil-adaptive manner. Specifically, we introduce a learning framework that adjusts the receptive fields on-the-go based on the current state of the observer's eye pupil to produce image effects that otherwise are not possible in current computer-generated holography approaches. We validate the proposed method both in simulations and on an experimental prototype holographic display, and demonstrate significant improvements in the depiction of depth-of-field effects, outperforming existing approaches both qualitatively and quantitatively by at least 5 dB in peak signal-to-noise ratio.