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Estimating Chair Pose Using ConvNets

The goal of our project is to align 3D models of chairs to 2D scene images containing chairs. In order to solve the alignment problem, we need to determine the translation, scale and rotation of the 3D object that maps it to the 2D image. For the purposes of this project we limit the scope to only azimuth orientation. We train a convolutional neural network on 64x64 rendered RGB chair images to predict the orientation of a previously unseen input. Our convolutional neural network consists of two convolutional layers with max pooling and ReLU activation functions and two fully connected layers also with ReLU activation functions. This simple architecture yielded surprisingly good results giving us 99% precision on the testing set. We evaluate the model on both a set of clean product images obtained from Bing and on cluttered natural images from the PASCAL VOC dataset and present a webcam interface that predicts chair orientation at interactive rates.