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

Novel View Synthesis has often relied on explicit 3D representations, which inject a strong 3D bias in the process; however, recent work has shown that network-based rendering can work better despite lacking 3D inductive biases. In this paper, we show that much better quality can be obtained by leveraging a strong 3D bias without a 3D representation. To do so, we introduce LagerNVS, an encoder-decoder network that uses 3D-aware features as a latent scene encoding. The encoder is initialized from a 3D reconstruction network, 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 results (including 31.1 PSNR on Re10k), with and without known cameras, renders in real-time, generalizes to in-the-wild data without known cameras, and can be paired with a diffusion decoder for generative completions.