# Stereo Magnification with Multi-Layer Images

arXiv: https://arxiv.org/abs/2201.05023 · HTML: https://mlsanity.com/paper/2201.05023/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 24 of 24)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [Neural Point-Based Graphics](https://mlsanity.com/paper/1906.08240/) (2019)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020)
- [Free View Synthesis](https://mlsanity.com/paper/2008.05511/) (2020)
- [NeX: Real-time View Synthesis with Neural Basis Expansion](https://mlsanity.com/paper/2103.05606/) (2021)
- [Stereo Magnification: Learning View Synthesis using Multiplane Images](https://mlsanity.com/paper/1805.09817/) (2018)
- [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020)
- [Pushing the Boundaries of View Extrapolation with Multiplane Images](https://mlsanity.com/paper/1905.00413/) (2019)
- [3D Ken Burns Effect from a Single Image](https://mlsanity.com/paper/1909.05483/) (2019)
- [Layer-structured 3D Scene Inference via View Synthesis](https://mlsanity.com/paper/1807.10264/) (2018)
- [Worldsheet: Wrapping the World in a 3D Sheet for View Synthesis from a Single Image](https://mlsanity.com/paper/2012.09854/) (2020)
- [MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images](https://mlsanity.com/paper/2008.06534/) (2020)
- [Deep Multi Depth Panoramas for View Synthesis](https://mlsanity.com/paper/2008.01815/) (2020)

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

- [Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review](https://mlsanity.com/paper/2405.03417/) (2024)
- [Quark: Real-time, High-resolution, and General Neural View Synthesis](https://mlsanity.com/paper/2411.16680/) (2024)
- [Tiled Multiplane Images for Practical 3D Photography](https://mlsanity.com/paper/2309.14291/) (2023)
- [LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors](https://mlsanity.com/paper/2412.09597/) (2024)
- [Bridging Implicit and Explicit Geometric Transformation for Single-Image View Synthesis](https://mlsanity.com/paper/2209.07105/) (2022)
- [Global Latent Neural Rendering](https://mlsanity.com/paper/2312.08338/) (2023)
- [Self-improving Multiplane-to-layer Images for Novel View Synthesis](https://mlsanity.com/paper/2210.01602/) (2022)
- [Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations](https://mlsanity.com/paper/2303.18139/) (2023)
- [DVSM: Decoder-only View Synthesis Model Done Right](https://mlsanity.com/paper/2605.29891/) (2026)
- [RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent](https://mlsanity.com/paper/2307.03017/) (2023)
- [fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations](https://mlsanity.com/paper/2312.16109/) (2023)

## Abstract (arXiv)

Representing scenes with multiple semi-transparent colored layers has been a popular and successful choice for real-time novel view synthesis. Existing approaches infer colors and transparency values over regularly-spaced layers of planar or spherical shape. In this work, we introduce a new view synthesis approach based on multiple semi-transparent layers with scene-adapted geometry. Our approach infers such representations from stereo pairs in two stages. The first stage infers the geometry of a small number of data-adaptive layers from a given pair of views. The second stage infers the color and the transparency values for these layers producing the final representation for novel view synthesis. Importantly, both stages are connected through a differentiable renderer and are trained in an end-to-end manner. In the experiments, we demonstrate the advantage of the proposed approach over the use of regularly-spaced layers with no adaptation to scene geometry. Despite being orders of magnitude faster during rendering, our approach also outperforms a recently proposed IBRNet system based on implicit geometry representation. See results at https://samsunglabs.github.io/StereoLayers .