  Camera relocalization involving a prior 3D reconstruction plays a crucial
role in many mixed reality and robotics applications. Estimating the camera
pose directly with respect to pre-built 3D models can be prohibitively
expensive for several applications with limited storage and/or communication
bandwidth. Although recent scene and absolute pose regression methods have
become popular for efficient camera localization, most of them are
computation-resource intensive and difficult to obtain a real-time inference
with high accuracy constraints. This study proposes a simple scene regression
method that requires only a multi-layer perceptron network for mapping scene
coordinates to achieve accurate camera pose estimations. The proposed approach
uses sparse descriptors to regress the scene coordinates, instead of a dense
RGB image. The use of sparse features provides several advantages. First, the
proposed regressor network is substantially smaller than those reported in
previous studies. This makes our system highly efficient and scalable. Second,
the pre-built 3D models provide the most reliable and robust 2D-3D matches.
Therefore, learning from them can lead to an awareness of equivalent features
and substantially improve the generalization performance. A detailed analysis
of our approach and extensive evaluations using existing datasets are provided
to support the proposed method. The implementation detail is available at
https://github.com/aislab/feat2map
