MuRF: Multi-Baseline Radiance Fields

Haofei Xu, Anpei Chen, Yuedong Chen, Christos Sakaridis, Yulun Zhang, Marc Pollefeys, Andreas Geiger, Fisher Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20041-20050

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.