Monte Carlo convolution for learning on non-uniformly sampled point clouds-Reference-Cited by-同舟云学术

Monte Carlo convolution for learning on non-uniformly sampled point clouds

Published:2018-12-31 Issue:6 Volume:37 Page:1-12
ISSN:0730-0301
Container-title:ACM Transactions on Graphics
language:en
Short-container-title:ACM Trans. Graph.

Author:

Hermosilla Pedro¹,Ritschel Tobias²,Vázquez Pere-Pau³,Vinacua Àlvar³,Ropinski Timo¹

Affiliation:

1. Ulm University, Germany

2. University College London, United Kingdom

3. Universitat Politècnica de Catalunya, Spain

Funder

Deutsche Forschungsgemeinschaft

Bundesministerium für Wirtschaft und Energie

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Link

https://dl.acm.org/doi/pdf/10.1145/3272127.3275110

Reference32 articles.

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2. Point convolutional neural networks by extension operators

3. Stochastic sampling in computer graphics

4. Angela Dai Angel X. Chang Manolis Savva Maciej Halber Thomas Funkhouser and Matthias Nießner. 2017. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. In CVPR. Angela Dai Angel X. Chang Manolis Savva Maciej Halber Thomas Funkhouser and Matthias Nießner. 2017. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. In CVPR.

5. Angela Dai and Matthias Nießner. 2018. 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. (2018). arXiv:1803.10409 Angela Dai and Matthias Nießner. 2018. 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. (2018). arXiv:1803.10409

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