bsc-thesis/introduction.tex

\subsection{Motivation \& Goals}

% introduce problem to sovle
% mention classical non deep-learning works, then say it would be nice to go end-to-end deep

Recently, SfM-Net \cite{} introduced an end-to-end deep learning approach for predicting depth
and dense optical flow in monocular image sequences based on estimating the 3D motion of individual objects and the camera.
SfM-Net predicts a batch of binary full image masks specyfing the object memberships of individual pixels with a standard encoder-decoder
network for pixel-wise prediction. A fully connected network branching off the encoder predicts a 3D motion for each object.
However, due to the fixed number of objects masks, it can only predict a small number of motions and
often fails to properly segment the pixels into the correct masks or assigns background pixels to object motions.

Thus, their approach is very unlikely to scale to dynamic scenes with a potentially
large number of diverse objects due to the inflexible nature of their instance segmentation technique.

A scalable approach to instance segmentation based on region-based convolutional networks
was recently introduced with Mask R-CNN \cite{}, which inherits the ability to detect
a large number of objects from a large number of classes at once from Faster R-CNN
and predicts pixel-precise segmentation masks for each detected object.

We propose \emph{Motion R-CNN}, which combines the scalable instance segmentation capabilities of
Mask R-CNN with the end-to-end 3D motion estimation approach introduced with SfM-Net.
For this, we naturally integrate 3D motion prediction for individual objects into the per-RoI R-CNN head
in parallel to classification and bounding box refinement.

\subsection{Related Work}

\paragraph{Deep optical flow estimation}
\paragraph{Deep scene flow estimation}
\paragraph{Structure from motion}
SfM-Net, SE3 Nets,


Behl2017ICCV