  Camera relocalization relies on 3D models of the scene with a large memory
footprint that is incompatible with the memory budget of several applications.
One solution to reduce the scene memory size is map compression by removing
certain 3D points and descriptor quantization. This achieves high compression
but leads to performance drop due to information loss. To address the memory
performance trade-off, we train a light-weight scene-specific auto-encoder
network that performs descriptor quantization-dequantization in an end-to-end
differentiable manner updating both product quantization centroids and network
parameters through back-propagation. In addition to optimizing the network for
descriptor reconstruction, we encourage it to preserve the descriptor-matching
performance with margin-based metric loss functions. Results show that for a
local descriptor memory of only 1MB, the synergistic combination of the
proposed network and map compression achieves the best performance on the
Aachen Day-Night compared to existing compression methods.
