@conference {Zheng2015,
	title = {Object proposals estimation in depth image using compact 3D shape manifolds},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {196{\textendash}208},
	abstract = {Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_16},
	author = {Zheng, Shuai and Prisacariu, Victor Adrian and Averkiou, Melinos and Cheng, Ming Ming and Mitra, Niloy J and Shotton, Jamie and Torr, Philip H.S. and Carsten Rother}
}
@article {Mitra2015,
	title = {Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut},
	volume = {34},
	number = {7},
	year = {2015},
	abstract = {Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10{\texttimes}) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.},
	keywords = {I46 [IMAGE PROCESSING AND COMPUTER VISION], partitioning, Segmentation{\textemdash}Region growing},
	url = {http://mftp.mmcheng.net/Papers/DenseCut.pdf},
	author = {Mitra, N J and Stam, J and Xu, K and Cheng, Ming-Ming and Prisacariu, Victor Adrian and Zheng, Shuai and Torr, Philip H S and Carsten Rother}
}
