# Fast View Synthesis with Deep Stereo Vision

arXiv: https://arxiv.org/abs/1804.09690 · HTML: https://mlsanity.com/paper/1804.09690/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior](https://mlsanity.com/paper/2303.14184/) (2023)
- [Deep View Synthesis via Self-Consistent Generative Network](https://mlsanity.com/paper/2101.10844/) (2021)
- [Neural Radiance Fields: Past, Present, and Future](https://mlsanity.com/paper/2304.10050/) (2023)
- [Deep Learning based Novel View Synthesis](https://mlsanity.com/paper/2107.06812/) (2021)
- [Novel View Synthesis from a Single Image via Unsupervised learning](https://mlsanity.com/paper/2110.15569/) (2021)
- [Beyond Darkness: Thermal-Supervised 3D Gaussian Splatting for Low-Light Novel View Synthesis](https://mlsanity.com/paper/2511.13011/) (2025)

## Abstract (arXiv)

Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being "solved". Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art. The code and supplementary material will be publicly available. Results could be found here https://youtu.be/5pzS9jc-5t0