MuRF: Multi-Baseline Radiance Fields
This addresses the problem of generating high-quality novel views from sparse inputs for applications in computer vision and graphics, representing an incremental improvement over prior methods.
The paper tackles sparse view synthesis under multiple baseline settings by introducing MuRF, a feed-forward approach that discretizes 3D space into target-aligned planes for efficient rendering, achieving state-of-the-art performance on datasets like DTU, RealEstate10K, and LLFF with demonstrated zero-shot generalization on Mip-NeRF 360.
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.