LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias

TL;DR

LVSM introduces two transformer-based models for novel view synthesis that do not rely on 3D inductive biases, achieving state-of-the-art results with improved scalability and generalization from sparse views.

Contribution

The paper presents LVSM, a fully data-driven transformer approach for view synthesis that eliminates traditional 3D biases, offering two architectures with superior performance.

Findings

Outperforms previous methods by 1.5 to 3.5 dB PSNR.

Achieves state-of-the-art quality across multiple datasets.

Operates efficiently with reduced computational resources.

Abstract

We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image tokens into a fixed number of 1D latent tokens, functioning as a fully learned scene representation, and decodes novel-view images from them; and (2) a decoder-only LVSM, which directly maps input images to novel-view outputs, completely eliminating intermediate scene representations. Both models bypass the 3D inductive biases used in previous methods -- from 3D representations (e.g., NeRF, 3DGS) to network designs (e.g., epipolar projections, plane sweeps) -- addressing novel view synthesis with a fully data-driven approach. While the encoder-decoder model offers faster inference due to its independent latent representation, the decoder-only LVSM achieves superior quality, scalability, and zero-shot generalization, outperforming previous state-of-the-art methods by 1.5 to 3.5 dB PSNR. Comprehensive evaluations across multiple datasets demonstrate that both LVSM variants achieve state-of-the-art novel view synthesis quality. Notably, our models surpass all previous methods even with reduced computational resources (1-2 GPUs). Please see our website for more details: https://haian-jin.github.io/projects/LVSM/ .