# Stereoscopic Neural Style Transfer

arXiv: https://arxiv.org/abs/1802.10591 · HTML: https://mlsanity.com/paper/1802.10591/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [NeRF-Art: Text-Driven Neural Radiance Fields Stylization](https://mlsanity.com/paper/2212.08070/) (2022)
- [Learning to Stylize Novel Views](https://mlsanity.com/paper/2105.13509/) (2021)
- [CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer](https://mlsanity.com/paper/2207.04808/) (2022)
- [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://mlsanity.com/paper/2112.00169/) (2021)
- [Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer](https://mlsanity.com/paper/2104.05376/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [SNeRF: Stylized Neural Implicit Representations for 3D Scenes](https://mlsanity.com/paper/2207.02363/) (2022)
- [Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior](https://mlsanity.com/paper/2105.03830/) (2021)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation](https://mlsanity.com/paper/2204.02111/) (2022)
- [Stereo Waterdrop Removal with Row-wise Dilated Attention](https://mlsanity.com/paper/2108.03457/) (2021)
- [NARRATE: A Normal Assisted Free-View Portrait Stylizer](https://mlsanity.com/paper/2207.00974/) (2022)
- [Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching](https://mlsanity.com/paper/2103.14333/) (2021)
- [Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data](https://mlsanity.com/paper/2308.10968/) (2023)

## Abstract (arXiv)

This paper presents the first attempt at stereoscopic neural style transfer, which responds to the emerging demand for 3D movies or AR/VR. We start with a careful examination of applying existing monocular style transfer methods to left and right views of stereoscopic images separately. This reveals that the original disparity consistency cannot be well preserved in the final stylization results, which causes 3D fatigue to the viewers. To address this issue, we incorporate a new disparity loss into the widely adopted style loss function by enforcing the bidirectional disparity constraint in non-occluded regions. For a practical real-time solution, we propose the first feed-forward network by jointly training a stylization sub-network and a disparity sub-network, and integrate them in a feature level middle domain. Our disparity sub-network is also the first end-to-end network for simultaneous bidirectional disparity and occlusion mask estimation. Finally, our network is effectively extended to stereoscopic videos, by considering both temporal coherence and disparity consistency. We will show that the proposed method clearly outperforms the baseline algorithms both quantitatively and qualitatively.