Kavli Affiliate: Matthew Fisher
| First 5 Authors: Chenxi Liu, Siqi Wang, Matthew Fisher, Deepali Aneja, Alec Jacobson
| Summary:
Effective representation of 2D images is fundamental in digital image
processing, where traditional methods like raster and vector graphics struggle
with sharpness and textural complexity respectively. Current neural fields
offer high-fidelity and resolution independence but require predefined meshes
with known discontinuities, restricting their utility. We observe that by
treating all mesh edges as potential discontinuities, we can represent the
magnitude of discontinuities with continuous variables and optimize. Based on
this observation, we introduce a novel discontinuous neural field model that
jointly approximate the target image and recovers discontinuities. Through
systematic evaluations, our neural field demonstrates superior performance in
denoising and super-resolution tasks compared to InstantNGP, achieving
improvements of over 5dB and 10dB, respectively. Our model also outperforms
Mumford-Shah-based methods in accurately capturing discontinuities, with
Chamfer distances 3.5x closer to the ground truth. Additionally, our approach
shows remarkable capability in handling complex artistic drawings and natural
images.
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