  We present a method for localizing a single camera with respect to a point
cloud map in indoor and outdoor scenes. The problem is challenging because
correspondences of local invariant features are inconsistent across the domains
between image and 3D. The problem is even more challenging as the method must
handle various environmental conditions such as illumination, weather, and
seasonal changes. Our method can match equirectangular images to the 3D range
projections by extracting cross-domain symmetric place descriptors. Our key
insight is to retain condition-invariant 3D geometry features from limited data
samples while eliminating the condition-related features by a designed
Generative Adversarial Network. Based on such features, we further design a
spherical convolution network to learn viewpoint-invariant symmetric place
descriptors. We evaluate our method on extensive self-collected datasets, which
involve \textit{Long-term} (variant appearance conditions),
\textit{Large-scale} (up to $2km$ structure/unstructured environment), and
\textit{Multistory} (four-floor confined space). Our method surpasses other
current state-of-the-arts by achieving around $3$ times higher place retrievals
to inconsistent environments, and above $3$ times accuracy on online
localization. To highlight our method's generalization capabilities, we also
evaluate the recognition across different datasets. With a single trained
model, i3dLoc can demonstrate reliable visual localization in random
conditions.
