MuRF: Multi-Baseline Radiance Fields
Haofei Xu Anpei Chen Yuedong Chen Christos Sakaridis Yulun Zhang Marc Pollefeys Andreas Geiger Fisher Yu
We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward
approach to solving sparse view synthesis under multiple different baseline
settings (small and large baselines, and different number of input views). To
render a target novel view, we discretize the 3D space into planes parallel to
the target image plane, and accordingly construct a target view frustum volume.
Such a target volume representation is spatially aligned with the target view,
which effectively aggregates relevant information from the input views for
high-quality rendering. It also facilitates subsequent radiance field
regression with a convolutional network thanks to its axis-aligned nature. The
3D context modeled by the convolutional network enables our method to synthesis
sharper scene structures than prior works. Our MuRF achieves state-of-the-art
performance across multiple different baseline settings and diverse scenarios
ranging from simple objects (DTU) to complex indoor and outdoor scenes
(RealEstate10K and LLFF). We also show promising zero-shot generalization
abilities on the Mip-NeRF 360 dataset, demonstrating the general applicability
of MuRF.