publication . Article . Preprint . Other literature type . 2018

Learning {3D} Shape Completion under Weak Supervision

Stutz, David; Geiger, Andreas;
Open Access English
  • Published: 29 Oct 2018
Abstract
We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow opti...
Subjects
free text keywords: Software, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science - Computer Vision and Pattern Recognition
102 references, page 1 of 7

Abramowitz M (1974) Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables. Dover Publications

Agarwal S, Mierle K, Others (2012) Ceres solver. http:// ceres-solver.org

Aubry M, Maturana D, Efros A, Russell B, Sivic J (2014) Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)

Bao S, Chandraker M, Lin Y, Savarese S (2013) Dense object reconstruction with semantic priors. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)

Besl P, McKay H (1992) A method for registration of 3d shapes. IEEE Trans on Pattern Analysis and Machine Intelligence (PAMI) 14:239{256 [OpenAIRE]

Blei DM, Kucukelbir A, McAuli e JD (2016) Variational inference: A review for statisticians. arXivorg 1601.00670

Brock A, Lim T, Ritchie JM, Weston N (2016) Generative and discriminative voxel modeling with convolutional neural networks. arXivorg 1608.04236

Chang AX, Funkhouser TA, Guibas LJ, Hanrahan P, Huang Q, Li Z, Savarese S, Savva M, Song S, Su H, Xiao J, Yi L, Yu F (2015) Shapenet: An information-rich 3d model repository. arXivorg 1512.03012 [OpenAIRE]

Chen X, Kundu K, Zhu Y, Ma H, Fidler S, Urtasun R (2016) 3d object proposals using stereo imagery for accurate object class detection. arXivorg 1608.07711

Choy CB, Xu D, Gwak J, Chen K, Savarese S (2016) 3d-r2n2: A uni ed approach for single and multi-view 3d object reconstruction. In: Proc. of the European Conf. on Computer Vision (ECCV)

Cicek O, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: Learning dense volumetric segmentation from sparse annotation. arXivorg 1606.06650

Cignoni P, Callieri M, Corsini M, Dellepiane M, Ganovelli F, Ranzuglia G (2008) Meshlab: an open-source mesh processing tool [OpenAIRE]

Collobert R, Kavukcuoglu K, Farabet C (2011) Torch7: A matlab-like environment for machine learning. In: Advances in Neural Information Processing Systems (NIPS) Workshops

Curless B, Levoy M (1996) A volumetric method for building complex models from range images. In: ACM Trans. on Graphics (SIGGRAPH) [OpenAIRE]

Dai A, Qi CR, Nie ner M (2017) Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)

102 references, page 1 of 7
Abstract
We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow opti...
Subjects
free text keywords: Software, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science - Computer Vision and Pattern Recognition
102 references, page 1 of 7

Abramowitz M (1974) Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables. Dover Publications

Agarwal S, Mierle K, Others (2012) Ceres solver. http:// ceres-solver.org

Aubry M, Maturana D, Efros A, Russell B, Sivic J (2014) Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)

Bao S, Chandraker M, Lin Y, Savarese S (2013) Dense object reconstruction with semantic priors. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)

Besl P, McKay H (1992) A method for registration of 3d shapes. IEEE Trans on Pattern Analysis and Machine Intelligence (PAMI) 14:239{256 [OpenAIRE]

Blei DM, Kucukelbir A, McAuli e JD (2016) Variational inference: A review for statisticians. arXivorg 1601.00670

Brock A, Lim T, Ritchie JM, Weston N (2016) Generative and discriminative voxel modeling with convolutional neural networks. arXivorg 1608.04236

Chang AX, Funkhouser TA, Guibas LJ, Hanrahan P, Huang Q, Li Z, Savarese S, Savva M, Song S, Su H, Xiao J, Yi L, Yu F (2015) Shapenet: An information-rich 3d model repository. arXivorg 1512.03012 [OpenAIRE]

Chen X, Kundu K, Zhu Y, Ma H, Fidler S, Urtasun R (2016) 3d object proposals using stereo imagery for accurate object class detection. arXivorg 1608.07711

Choy CB, Xu D, Gwak J, Chen K, Savarese S (2016) 3d-r2n2: A uni ed approach for single and multi-view 3d object reconstruction. In: Proc. of the European Conf. on Computer Vision (ECCV)

Cicek O, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: Learning dense volumetric segmentation from sparse annotation. arXivorg 1606.06650

Cignoni P, Callieri M, Corsini M, Dellepiane M, Ganovelli F, Ranzuglia G (2008) Meshlab: an open-source mesh processing tool [OpenAIRE]

Collobert R, Kavukcuoglu K, Farabet C (2011) Torch7: A matlab-like environment for machine learning. In: Advances in Neural Information Processing Systems (NIPS) Workshops

Curless B, Levoy M (1996) A volumetric method for building complex models from range images. In: ACM Trans. on Graphics (SIGGRAPH) [OpenAIRE]

Dai A, Qi CR, Nie ner M (2017) Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)

102 references, page 1 of 7
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue