22 research outputs found
Using Self-Contradiction to Learn Confidence Measures in Stereo Vision
Learned confidence measures gain increasing importance for outlier removal
and quality improvement in stereo vision. However, acquiring the necessary
training data is typically a tedious and time consuming task that involves
manual interaction, active sensing devices and/or synthetic scenes. To overcome
this problem, we propose a new, flexible, and scalable way for generating
training data that only requires a set of stereo images as input. The key idea
of our approach is to use different view points for reasoning about
contradictions and consistencies between multiple depth maps generated with the
same stereo algorithm. This enables us to generate a huge amount of training
data in a fully automated manner. Among other experiments, we demonstrate the
potential of our approach by boosting the performance of three learned
confidence measures on the KITTI2012 dataset by simply training them on a vast
amount of automatically generated training data rather than a limited amount of
laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE
(https://www.ieee.org). The official version of the paper will be made
available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of
the paper also contains the supplementary material, which will not appear
IEEE Xplore (R
DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction
Deep Neural Networks (DNNs) have the potential to improve the quality of
image-based 3D reconstructions. However, the use of DNNs in the context of 3D
reconstruction from large and high-resolution image datasets is still an open
challenge, due to memory and computational constraints. We propose a pipeline
which takes advantage of DNNs to improve the quality of 3D reconstructions
while being able to handle large and high-resolution datasets. In particular,
we propose a confidence prediction network explicitly tailored for Multi-View
Stereo (MVS) and we use it for both depth map outlier filtering and depth map
refinement within our pipeline, in order to improve the quality of the final 3D
reconstructions. We train our confidence prediction network on (semi-)dense
ground truth depth maps from publicly available real world MVS datasets. With
extensive experiments on popular benchmarks, we show that our overall pipeline
can produce state-of-the-art 3D reconstructions, both qualitatively and
quantitatively.Comment: changes in V3: re-worked confidence prediction scheme, re-organized
text, updated experiments; changes in V2: a reference was update