  Although many recent works have investigated generalizable NeRF-based novel
view synthesis for unseen scenes, they seldom consider the synthetic-to-real
generalization, which is desired in many practical applications. In this work,
we first investigate the effects of synthetic data in synthetic-to-real novel
view synthesis and surprisingly observe that models trained with synthetic data
tend to produce sharper but less accurate volume densities. For pixels where
the volume densities are correct, fine-grained details will be obtained.
Otherwise, severe artifacts will be produced. To maintain the advantages of
using synthetic data while avoiding its negative effects, we propose to
introduce geometry-aware contrastive learning to learn multi-view consistent
features with geometric constraints. Meanwhile, we adopt cross-view attention
to further enhance the geometry perception of features by querying features
across input views. Experiments demonstrate that under the synthetic-to-real
setting, our method can render images with higher quality and better
fine-grained details, outperforming existing generalizable novel view synthesis
methods in terms of PSNR, SSIM, and LPIPS. When trained on real data, our
method also achieves state-of-the-art results.
