825 research outputs found
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201
End-to-End Localization and Ranking for Relative Attributes
We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201
A Survey on Deep Learning Technique for Video Segmentation
Video segmentation -- partitioning video frames into multiple segments or
objects -- plays a critical role in a broad range of practical applications,
from enhancing visual effects in movie, to understanding scenes in autonomous
driving, to creating virtual background in video conferencing. Recently, with
the renaissance of connectionism in computer vision, there has been an influx
of deep learning based approaches for video segmentation that have delivered
compelling performance. In this survey, we comprehensively review two basic
lines of research -- generic object segmentation (of unknown categories) in
videos, and video semantic segmentation -- by introducing their respective task
settings, background concepts, perceived need, development history, and main
challenges. We also offer a detailed overview of representative literature on
both methods and datasets. We further benchmark the reviewed methods on several
well-known datasets. Finally, we point out open issues in this field, and
suggest opportunities for further research. We also provide a public website to
continuously track developments in this fast advancing field:
https://github.com/tfzhou/VS-Survey.Comment: Accepted by TPAMI. Website: https://github.com/tfzhou/VS-Surve
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