388 research outputs found
Geometry meets semantics for semi-supervised monocular depth estimation
Depth estimation from a single image represents a very exciting challenge in
computer vision. While other image-based depth sensing techniques leverage on
the geometry between different viewpoints (e.g., stereo or structure from
motion), the lack of these cues within a single image renders ill-posed the
monocular depth estimation task. For inference, state-of-the-art
encoder-decoder architectures for monocular depth estimation rely on effective
feature representations learned at training time. For unsupervised training of
these models, geometry has been effectively exploited by suitable images
warping losses computed from views acquired by a stereo rig or a moving camera.
In this paper, we make a further step forward showing that learning semantic
information from images enables to improve effectively monocular depth
estimation as well. In particular, by leveraging on semantically labeled images
together with unsupervised signals gained by geometry through an image warping
loss, we propose a deep learning approach aimed at joint semantic segmentation
and depth estimation. Our overall learning framework is semi-supervised, as we
deploy groundtruth data only in the semantic domain. At training time, our
network learns a common feature representation for both tasks and a novel
cross-task loss function is proposed. The experimental findings show how,
jointly tackling depth prediction and semantic segmentation, allows to improve
depth estimation accuracy. In particular, on the KITTI dataset our network
outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201
Unsupervised Learning of Depth and Ego-Motion from Video
We present an unsupervised learning framework for the task of monocular depth
and camera motion estimation from unstructured video sequences. We achieve this
by simultaneously training depth and camera pose estimation networks using the
task of view synthesis as the supervisory signal. The networks are thus coupled
via the view synthesis objective during training, but can be applied
independently at test time. Empirical evaluation on the KITTI dataset
demonstrates the effectiveness of our approach: 1) monocular depth performing
comparably with supervised methods that use either ground-truth pose or depth
for training, and 2) pose estimation performing favorably with established SLAM
systems under comparable input settings.Comment: Accepted to CVPR 2017. Project webpage:
https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner
Self-Supervised Relative Depth Learning for Urban Scene Understanding
As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods
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