  The rapid development of inexpensive commodity depth sensors has made
keypoint detection and matching in the depth image modality an important
problem in computer vision. Despite great improvements in recent RGB local
feature learning methods, adapting them directly in the depth modality leads to
unsatisfactory performance. Most of these methods do not explicitly reason
beyond the visible pixels in the images. To address the limitations of these
methods, we propose a framework ViewSynth, to jointly learn: (1) viewpoint
invariant keypoint-descriptor from depth images using a proposed Contrastive
Matching Loss, and (2) view synthesis of depth images from different viewpoints
using the proposed View Synthesis Module and View Synthesis Loss. By learning
view synthesis, we explicitly encourage the feature extractor to encode
information about not only the visible, but also the occluded parts of the
scene. We demonstrate that in the depth modality, ViewSynth outperforms the
state-of-the-art depth and RGB local feature extraction techniques in the 3D
keypoint matching and camera localization tasks on the RGB-D datasets 7-Scenes,
TUM RGBD and CoRBS in most scenarios. We also show the generalizability of
ViewSynth in 3D keypoint matching across different datasets.
