mlsanityData to 7 Oct 2026

LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis

arXiv 2603.20176 ↗First posted 20 Mar 2026cs.CVcited by 4

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Abstract (arXiv)

Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive biases are still helpful in the design of such networks. We show this point by introducing LagerNVS, an encoder-decoder neural network for NVS that builds on `3D-aware' latent features. The encoder is initialized from a 3D reconstruction network pre-trained using explicit 3D supervision. This is paired with a lightweight decoder, and trained end-to-end with photometric losses. LagerNVS achieves state-of-the-art deterministic feed-forward Novel View Synthesis (including 31.4 PSNR on Re10k), with and without known cameras, renders in real time, generalizes to in-the-wild data, and can be paired with a diffusion decoder for generative extrapolation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 78 of its 91 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

78 of 91 references.

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026VGGT-Omegacited by 20
  2. 2026RayViT: Ray-Conditioned Visual Representations for Viewpoint-Robust Imitation Learningcited by 0
  3. 2026RoGe: Novel View Synthesis via End-to-End Implicit Reconstruction and Generationcited by 0
  4. 2026VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesiscited by 0