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Title: Using Synthetic Data Generation to Probe Multi-View Stereo Networks
Synthetic data is highly useful for training machine learning systems performing image-based 3D reconstruction, as synthetic data has applications in both extending existing generalizable datasets and being tailored to train neural networks for specific learning tasks of interest. In this paper, we introduce and utilize a synthetic data generation suite capable of generating data given existing 3D scene models as input. Specifically, we use our tool to generate image sequences for use with Multi-View Stereo (MVS), moving a camera through the virtual space according to user-chosen camera parameters. We evaluate how the given camera parameters and type of 3D environment affect how applicable the generated image sequences are to the MVS task using five pre-trained neural networks on image sequences generated from three different 3D scene datasets. We obtain generated predictions for each combination of parameter value and input image sequence, using standard error metrics to analyze the differences in depth predictions on image sequences across 3D datasets, parameters, and networks. Among other results, we find that camera height and vertical camera viewing angle are the parameters that cause the most variation in depth prediction errors on these image sequences.
Authors:
; ; ; ; ; ;
Award ID(s):
1911230
Publication Date:
NSF-PAR ID:
10332456
Journal Name:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Page Range or eLocation-ID:
1583 to 1591
Sponsoring Org:
National Science Foundation
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