  Humans naturally perceive a 3D scene in front of them through accumulation of
information obtained from multiple interconnected projections of the scene and
by interpreting their correspondence. This phenomenon has inspired artificial
intelligence models to extract the depth and view angle of the observed scene
by modeling the correspondence between different views of that scene. Our paper
is built upon previous works in the field of unsupervised depth and relative
camera pose estimation from temporal consecutive video frames using deep
learning (DL) models. Our approach uses a hybrid learning framework introduced
in a recent work called GeoNet, which leverages geometric constraints in the 3D
scenes to synthesize a novel view from intermediate DL-based predicted depth
and relative pose. However, the state-of-the-art unsupervised depth and pose
estimation DL models are exclusively trained/tested on a few available outdoor
scene datasets and we have shown they are hardly transferable to new scenes,
especially from indoor environments, in which estimation requires higher
precision and dealing with probable occlusions. This paper introduces "Indoor
GeoNet", a weakly supervised depth and camera pose estimation model targeted
for indoor scenes. In Indoor GeoNet, we take advantage of the availability of
indoor RGBD datasets collected by human or robot navigators, and added partial
(i.e. weak) supervision in depth training into the model. Experimental results
showed that our model effectively generalizes to new scenes from different
buildings. Indoor GeoNet demonstrated significant depth and pose estimation
error reduction when compared to the original GeoNet, while showing 3 times
more reconstruction accuracy in synthesizing novel views in indoor
environments.
