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33
approach.tex
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@ -39,13 +39,13 @@ $o_t^{cam}$& softmax, 2 & 1 $\times$ 2 \\
\midrule
\multicolumn{3}{c}{\textbf{RoI Head: Motions}}\\
\midrule
& From M$_0$: flatten & N$_{RPN}$ $\times$ 7 $\cdot$ 7 $\cdot$ 256 \\
T$_1$ & $\begin{bmatrix}\textrm{fully connected}, 1024\end{bmatrix}$ $\times$ 2 & N$_{RPN}$ $\times$ 1024 \\
$\forall k: R_t^k$ & From T$_1$: fully connected, 3 & N$_{RPN}$ $\times$ 3 \\
$\forall k: t_t^k$ & From T$_1$: fully connected, 3 & N$_{RPN}$ $\times$ 3 \\
$\forall k: p_t^k$ & From T$_1$: fully connected, 3 & N$_{RPN}$ $\times$ 3 \\
& From T$_1$: fully connected, 2 & N$_{RPN}$ $\times$ 2 \\
$\forall k: o_t^k$ & softmax, 2 & N$_{RPN}$ $\times$ 2 \\
& From M$_0$: flatten & N$_{RoI}$ $\times$ 7 $\cdot$ 7 $\cdot$ 256 \\
T$_1$ & $\begin{bmatrix}\textrm{fully connected}, 1024\end{bmatrix}$ $\times$ 2 & N$_{RoI}$ $\times$ 1024 \\
$\forall k: R_t^k$ & From T$_1$: fully connected, 3 & N$_{RoI}$ $\times$ 3 \\
$\forall k: t_t^k$ & From T$_1$: fully connected, 3 & N$_{RoI}$ $\times$ 3 \\
$\forall k: p_t^k$ & From T$_1$: fully connected, 3 & N$_{RoI}$ $\times$ 3 \\
& From T$_1$: fully connected, 2 & N$_{RoI}$ $\times$ 2 \\
$\forall k: o_t^k$ & softmax, 2 & N$_{RoI}$ $\times$ 2 \\
\bottomrule
\end{tabular}
@ -88,13 +88,13 @@ $o_t^{cam}$& softmax, 2 & 1 $\times$ 2 \\
\midrule
\multicolumn{3}{c}{\textbf{RoI Head: Motions}}\\
\midrule
& From M$_1$: flatten & N$_{RPN}$ $\times$ 14 $\cdot$ 14 $\cdot$ 256 \\
T$_3$ & $\begin{bmatrix}\textrm{fully connected}, 1024\end{bmatrix}$ $\times$ 2 & N$_{RPN}$ $\times$ 1024 \\
$\forall k: R_t^k$ & From T$_3$: fully connected, 3 & N$_{RPN}$ $\times$ 3 \\
$\forall k: t_t^k$ & From T$_3$: fully connected, 3 & N$_{RPN}$ $\times$ 3 \\
$\forall k: p_t^k$ & From T$_3$: fully connected, 3 & N$_{RPN}$ $\times$ 3 \\
& From T$_2$: fully connected, 2 & N$_{RPN}$ $\times$ 2 \\
$\forall k: o_t^k$ & softmax, 2 & N$_{RPN}$ $\times$ 2 \\
& From M$_1$: flatten & N$_{RoI}$ $\times$ 14 $\cdot$ 14 $\cdot$ 256 \\
T$_3$ & $\begin{bmatrix}\textrm{fully connected}, 1024\end{bmatrix}$ $\times$ 2 & N$_{RoI}$ $\times$ 1024 \\
$\forall k: R_t^k$ & From T$_3$: fully connected, 3 & N$_{RoI}$ $\times$ 3 \\
$\forall k: t_t^k$ & From T$_3$: fully connected, 3 & N$_{RoI}$ $\times$ 3 \\
$\forall k: p_t^k$ & From T$_3$: fully connected, 3 & N$_{RoI}$ $\times$ 3 \\
& From T$_2$: fully connected, 2 & N$_{RoI}$ $\times$ 2 \\
$\forall k: o_t^k$ & softmax, 2 & N$_{RoI}$ $\times$ 2 \\
\bottomrule
\end{tabular}
@ -324,6 +324,11 @@ loss could benefit motion regression by removing any loss balancing issues betwe
rotation, translation and pivot terms \cite{PoseNet2},
which can make it interesting even when 3D motion ground truth is available.
\subsection{Inference}
\label{ssec:inference}
During inference, we proceed analogously to Mask R-CNN.
In the same way as the RoI mask head, at test time, we compute the RoI motion head
from the features extracted with refined bounding boxes.
\subsection{Dense flow from motion}
\label{ssec:postprocessing}

2
background.tex
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@ -439,7 +439,7 @@ Figure from \cite{FPN}.
\label{figure:fpn_block}
\end{figure}
\subsection{Training Mask R-CNN}
\subsection{Mask R-CNN: Training and Inference}
\paragraph{Loss definitions}
For regression, we define the smooth $\ell_1$ regression loss as
\begin{equation}