  Accurate lighting estimation is challenging yet critical to many computer
vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.
Existing approaches model lighting in either frequency domain or spatial domain
which is insufficient to represent the complex lighting conditions in scenes
and tends to produce inaccurate estimation. This paper presents NeedleLight, a
new lighting estimation model that represents illumination with needlets and
allows lighting estimation in both frequency domain and spatial domain jointly.
An optimal thresholding function is designed to achieve sparse needlets which
trims redundant lighting parameters and demonstrates superior localization
properties for illumination representation. In addition, a novel spherical
transport loss is designed based on optimal transport theory which guides to
regress lighting representation parameters with consideration of the spatial
information. Furthermore, we propose a new metric that is concise yet effective
by directly evaluating the estimated illumination maps rather than rendered
images. Extensive experiments show that NeedleLight achieves superior lighting
estimation consistently across multiple evaluation metrics as compared with
state-of-the-art methods.
