  Radiance field is an effective representation of 3D scenes, which has been
widely adopted in novel-view synthesis and 3D reconstruction. It is still an
open and challenging problem to evaluate the geometry, i.e., the density field,
as the ground-truth is almost impossible to be obtained. One alternative
indirect solution is to transform the density field into a point-cloud and
compute its Chamfer Distance with the scanned ground-truth. However, many
widely-used datasets have no point-cloud ground-truth since the scanning
process along with the equipment is expensive and complicated. To this end, we
propose a novel metric, named Inverse Mean Residual Color (IMRC), which can
evaluate the geometry only with the observation images. Our key insight is that
the better the geometry is, the lower-frequency the computed color field is.
From this insight, given reconstructed density field and the observation
images, we design a closed-form method to approximate the color field with
low-frequency spherical harmonics and compute the inverse mean residual color.
Then the higher the IMRC, the better the geometry. Qualitative and quantitative
experimental results verify the effectiveness of our proposed IMRC metric. We
also benchmark several state-of-the-art methods using IMRC to promote future
related research.

Computer Vision and Pattern Recognition