mlsanityData to 7 Oct 2026

LiveView: Dynamic Target-Centered MPI for View Synthesis

arXiv 2107.05113 ↗First posted 11 Jul 2021cs.CVcited by 4

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Abstract (arXiv)

Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass. These methods produce fast, high-quality rendering of novel views, but rely on slow and computationally expensive MPI generation methods unsuitable for real-time applications. In addition, most MPI techniques use fixed depth/disparity planes which cannot be modified once the training is complete, hence offering very little flexibility at run-time. We propose LiveView - a novel MPI generation and rendering technique that produces high-quality view synthesis in real-time. Our method can also offer the flexibility to select scene-dependent MPI planes (number of planes and spacing between them) at run-time. LiveView first warps input images to target view (target-centered) and then learns to generate a target view centered MPI, one depth plane at a time (dynamically). The method generates high-quality renderings, while also enabling fast MPI generation and novel view synthesis. As a result, LiveView enables real-time view synthesis applications where an MPI needs to be updated frequently based on a video stream of input views. We demonstrate that LiveView improves the quality of view synthesis while being 70 times faster at run-time compared to state-of-the-art MPI-based methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 11 Jul 2021.

Builds on

10 of 32 references.

References · 10

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  3. 2020Neural Sparse Voxel Fieldscited by 959
  4. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 857
  5. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  6. 2020Single-View View Synthesis with Multiplane Imagescited by 263
  7. 2019DeepView: View Synthesis with Learned Gradient Descentcited by 253
  8. 2019Pushing the Boundaries of View Extrapolation with Multiplane Imagescited by 191
  9. 2015DeepStereo: Learning to Predict New Views from the World's Imagerycited by 185
  10. 2016Learning-Based View Synthesis for Light Field Camerascited by 145

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Cited by year

Citing papers per year: 2022: 1; 2023: 3.

4 papers.