mkdir build
cd build
cmake .. -DCUDA_PROPAGATE_HOST_FLAGS=0
make -j8

./opengl_depth_rendering ../data/room_89_simple.obj

\usepackage[table]{xcolor}
\definecolor{bedColor}{rgb}{0, 0, 1}
\definecolor{booksColor}{rgb}{0.9137,0.3490,0.1882}
\definecolor{ceilColor}{rgb}{0, 0.8549, 0}
\definecolor{chairColor}{rgb}{0.5843,0,0.9412}
\definecolor{floorColor}{rgb}{0.8706,0.9451,0.0941}
\definecolor{furnColor}{rgb}{1.0000,0.8078,0.8078}
\definecolor{objsColor}{rgb}{0,0.8784,0.8980}
\definecolor{paintColor}{rgb}{0.4157,0.5333,0.8000}
\definecolor{sofaColor}{rgb}{0.4588,0.1137,0.1608}
\definecolor{tableColor}{rgb}{0.9412,0.1373,0.9216}
\definecolor{tvColor}{rgb}{0,0.6549,0.6118}
\definecolor{wallColor}{rgb}{0.9765,0.5451,0}
\definecolor{windColor}{rgb}{0.8824,0.8980,0.7608}

\begin{table*}
\begin{tabular}{l}
\textbf{13 class semantic segmentation: NYUv2} \\
\end{tabular}
\centering
\begin{tabular}{|l|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|p{0.5cm}|}
\hline
Training  & \cellcolor{bedColor}\rotatebox{90}{bed} & \cellcolor{booksColor}\rotatebox{90}{books}  & \cellcolor{ceilColor}\rotatebox{90}{ceil.} & \cellcolor{chairColor}\rotatebox{90}{chair}  & \cellcolor{floorColor}\rotatebox{90}{floor}  & \cellcolor{furnColor}\rotatebox{90}{furn}   & \cellcolor{objsColor}\rotatebox{90}{objs.} & \cellcolor{paintColor}\rotatebox{90}{paint.} & \cellcolor{sofaColor}\rotatebox{90}{sofa}   & \cellcolor{tableColor}\rotatebox{90}{table}  & \cellcolor{tvColor}\rotatebox{90}{tv}     & \cellcolor{wallColor}\rotatebox{90}{wall}   & \cellcolor{windColor}\rotatebox{90}{window} \\ \hline
NYU-DHA & 67.7 & 6.5 & 69.9 & 47.9 & \textbf{96.2} & 53.8 & 46.5 & 11.3 & 50.7 & 41.6 & 10.8 & 85.0 & 25.8 \\ \hline
SceneNet-DHA & 60.8 & 2.0 & 44.2 & 68.3 & 90.2 & 26.4 & 27.6 &  6.3 & 21.1 & 42.2 & 0 & \textbf{92.0} & 0.0 \\ \hline
SceneNet-FT-NYU-DHA & 70.8 & 5.3 & 75.0 & 58.9 & 95.9 & 63.3 & 48.4 & 15.2 & 58.0 & 43.6 & 22.3 &  85.1  & 29.9 \\ \hline
NYU-DO-DHA & 69.6 & 3.1 & 69.3 & 53.2 & 95.9 & 60.0 & 49.0 & 11.6 & 52.7 & 40.2 & 17.3 & 85.0 & 27.1 \\ \hline
SceneNet-DO-DHA & 67.9 & 4.7 & 41.2 & 67.7 & 87.9 & 38.4 & 25.6 &  6.3 & 16.3 & 43.8 & 0 & 88.6 & 1.0 \\ \hline
SceneNet-FT-NYU-DO-DHA & \textbf{70.8} & 5.5 & 76.2 & 59.6 & 95.9 & \textbf{62.3} & \textbf{50.0} & 18.0 & \textbf{61.3} & 42.2 & 22.2 & 86.1 & 32.1 \\ \hline
Eigen \textit{et al.} (rgbd+normals) \cite{Eigen:etal:ICCV2015} & 61.1 & \textbf{49.7} & 78.3 & \textbf{72.1} & 96.0 & 55.1 & 40.7 &\textbf{58.7} & 45.8 &\textbf{44.9}& \textbf{41.9} & 88.7 & \textbf{57.7}  \\ \hline
Hermans \textit{et al.}(rgbd+crf)\cite{Hermans:etal:ICRA2014} & 68.4 & N/A & \textbf{83.4} & 41.9 & 91.5 & 37.1 & 8.6 & N/A & 28.5 & 27.7 & 38.4 & 71.8 & 46.1 \\ \hline
\end{tabular}
\vspace{0.5mm} \vspace{0.5mm}
\caption{Results on NYUv2 test data for 13 semantic classes. We see a similar pattern here --- adding synthetic data helps immensely in improving the performance of nearly all functional categories of objects using DHA as input channels. As expected, accuracy on \textit{books}, \textit{painting}, \textit{tv}, and \textit{windows}, is compromised highlighting that the role of depth as a modality to segment these objects is limited. Note that we recomputed the accuracies of \cite{Eigen:etal:ICCV2015} using their publicly available annotations of 320$\times$240 and resizing them to 224$\times$224. Hermans \textit{et al.} \cite{Hermans:etal:ICRA2014} use ``\textit{Decoration}" and ``\textit{Bookshelf}" instead of \textit{painting} and \textit{books} as the other two classes. Therefore, they are not directly comparable. Also, their annotations are not publicly available but we have still added their results in the table. Note that they use 640$\times$480. Poor performance of SceneNet-DHA and SceneNet-DO-DHA on \textit{tv} and \textit{windows} is mainly due to limited training data for these classes in SceneNet.}
\label{table: CA breakdown for 13 classes}
\end{table*}

@inproceedings{Handa:etal:ICRA2016,
  author    = {Ankur Handa and 
               Viorica P{\u a}tr{\u a}ucean and
               Simon Stent and
               Roberto Cipolla},
  title     = {SceneNet: an Annotated Model Generator for Indoor Scene Understanding},
  booktitle = {ICRA},
  year      = {2016}
}

@inproceedings{Handa:etal:CVPR2016,
  author    = {Ankur Handa and 
               Viorica P{\u a}tr{\u a}ucean and
               Vijay Badrinarayanan and 
               Simon Stent and
               Roberto Cipolla},
  title     = {Understanding Real World Indoor Scenes With Synthetic Data},
  booktitle = {CVPR},
  year      = {2016}
}

@inproceedings{Handa:etal:arXiv2016,
  author    = {Ankur Handa and 
               Viorica P{\u a}tr{\u a}ucean and
               Vijay Badrinarayanan and 
               Simon Stent and
               Roberto Cipolla},
  title     = {SceneNet: Understanding Real World Indoor Scenes With Synthetic Data},
  booktitle = {arXiv},
  year      = {2015}
}