%0 Generic %1 zou20173dprnn %A Zou, Chuhang %A Yumer, Ersin %A Yang, Jimei %A Ceylan, Duygu %A Hoiem, Derek %D 2017 %K 2017 3D arxiv computer-vision deep-learning iccv paper rnn shape %T 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks %U http://arxiv.org/abs/1708.01648 %X The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives. Given a single depth image of an object, we present 3D-PRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives. Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation. We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network. We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxel-based generative models while using a significantly reduced parameter space.

@misc{zou20173dprnn, abstract = {The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives. Given a single depth image of an object, we present 3D-PRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives. Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation. We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network. We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxel-based generative models while using a significantly reduced parameter space.}, added-at = {2018-04-05T13:47:43.000+0200}, author = {Zou, Chuhang and Yumer, Ersin and Yang, Jimei and Ceylan, Duygu and Hoiem, Derek}, biburl = {https://www.bibsonomy.org/bibtex/279bd123c63bb4d6def2179108207906d/achakraborty}, description = {[1708.01648] 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks}, interhash = {c6e35e4a7de1d8a7ac96a4e357f681bb}, intrahash = {79bd123c63bb4d6def2179108207906d}, keywords = {2017 3D arxiv computer-vision deep-learning iccv paper rnn shape}, note = {cite arxiv:1708.01648Comment: ICCV 2017}, timestamp = {2018-04-05T13:48:13.000+0200}, title = {3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks}, url = {http://arxiv.org/abs/1708.01648}, year = 2017 }