# Geometry-aware Deep Network for Single-Image Novel View Synthesis

arXiv: https://arxiv.org/abs/1804.06008 · HTML: https://mlsanity.com/paper/1804.06008/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 19 of 19)

- [Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior](https://mlsanity.com/paper/2303.14184/) (2023)
- [Hallucinated Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2111.15246/) (2021)
- [Geometry-Free View Synthesis: Transformers and no 3D Priors](https://mlsanity.com/paper/2104.07652/) (2021)
- [Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery](https://mlsanity.com/paper/2103.01623/) (2021)
- [Self-Supervised Visibility Learning for Novel View Synthesis](https://mlsanity.com/paper/2103.15407/) (2021)
- [Deep View Synthesis via Self-Consistent Generative Network](https://mlsanity.com/paper/2101.10844/) (2021)
- [Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates](https://mlsanity.com/paper/2309.11281/) (2023)
- [MOWA: Multiple-in-One Image Warping Model](https://mlsanity.com/paper/2404.10716/) (2024)
- [SplatFlow: Learning Multi-frame Optical Flow via Splatting](https://mlsanity.com/paper/2306.08887/) (2023)
- [DMS:Diffusion-Based Multi-Baseline Stereo Generation for Improving Self-Supervised Depth Estimation](https://mlsanity.com/paper/2508.13091/) (2025)
- [LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference](https://mlsanity.com/paper/2307.12217/) (2023)
- [Appearance Editing with Free-viewpoint Neural Rendering](https://mlsanity.com/paper/2110.07674/) (2021)
- [Novel View Synthesis from a Single Image via Unsupervised learning](https://mlsanity.com/paper/2110.15569/) (2021)
- [Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis](https://mlsanity.com/paper/2204.00156/) (2022)
- [Learning Pose Image Manifolds Using Geometry-Preserving GANs and Elasticae](https://mlsanity.com/paper/2305.10513/) (2023)
- [Depth self-supervision for single image novel view synthesis](https://mlsanity.com/paper/2308.14108/) (2023)
- [An Artificial Intelligence Approach for Interpreting Creative Combinational Designs](https://mlsanity.com/paper/2405.04985/) (2024)
- [Inverse Image-Based Rendering for Light Field Generation from Single Images](https://mlsanity.com/paper/2510.20132/) (2025)
- [Spackle: Completing Large View Single Image NVS with Adaptive Gaussians](https://mlsanity.com/paper/2609.30941/) (2026)

## Abstract (arXiv)

This paper tackles the problem of novel view synthesis from a single image. In particular, we target real-world scenes with rich geometric structure, a challenging task due to the large appearance variations of such scenes and the lack of simple 3D models to represent them. Modern, learning-based approaches mostly focus on appearance to synthesize novel views and thus tend to generate predictions that are inconsistent with the underlying scene structure. By contrast, in this paper, we propose to exploit the 3D geometry of the scene to synthesize a novel view. Specifically, we approximate a real-world scene by a fixed number of planes, and learn to predict a set of homographies and their corresponding region masks to transform the input image into a novel view. To this end, we develop a new region-aware geometric transform network that performs these multiple tasks in a common framework. Our results on the outdoor KITTI and the indoor ScanNet datasets demonstrate the effectiveness of our network in generating high quality synthetic views that respect the scene geometry, thus outperforming the state-of-the-art methods.