← 研踪 PaperTrailCVF · CVPR 2026:cvf:0fffc628efcef48ba3e05f3db5524ff5 · 记录 #1
LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:57
摘要
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. See szymanowiczs.github.io/lagernvs for code, models, and examples.
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记录 #1 · 2026 · 最新观测版本
LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis
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. See szymanowiczs.github.io/lagernvs for code, models, and examples.
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