  Reconstructing an object from photos and placing it virtually in a new
environment goes beyond the standard novel view synthesis task as the
appearance of the object has to not only adapt to the novel viewpoint but also
to the new lighting conditions and yet evaluations of inverse rendering methods
rely on novel view synthesis data or simplistic synthetic datasets for
quantitative analysis. This work presents a real-world dataset for measuring
the reconstruction and rendering of objects for relighting. To this end, we
capture the environment lighting and ground truth images of the same objects in
multiple environments allowing to reconstruct the objects from images taken in
one environment and quantify the quality of the rendered views for the unseen
lighting environments. Further, we introduce a simple baseline composed of
off-the-shelf methods and test several state-of-the-art methods on the
relighting task and show that novel view synthesis is not a reliable proxy to
measure performance. Code and dataset are available at
https://github.com/isl-org/objects-with-lighting .
