27,969 research outputs found
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
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
This work tackles scene understanding for outdoor robotic navigation, solely
relying on images captured by an on-board camera. Conventional visual scene
understanding interprets the environment based on specific descriptive
categories. However, such a representation is not directly interpretable for
decision-making and constrains robot operation to a specific domain. Thus, we
propose to segment egocentric images directly in terms of how a robot can
navigate in them, and tailor the learning problem to an autonomous navigation
task. Building around an image segmentation network, we present a generic
affordance consisting of 3 driveability levels which can broadly apply to both
urban and off-road scenes. By encoding these levels with soft ordinal labels,
we incorporate inter-class distances during learning which improves
segmentation compared to standard "hard" one-hot labelling. In addition, we
propose a navigation-oriented pixel-wise loss weighting method which assigns
higher importance to safety-critical areas. We evaluate our approach on
large-scale public image segmentation datasets ranging from sunny city streets
to snowy forest trails. In a cross-dataset generalization experiment, we show
that our affordance learning scheme can be applied across a diverse mix of
datasets and improves driveability estimation in unseen environments compared
to general-purpose, single-dataset segmentation.Comment: Accepted in Robotics and Automation Letters (RA-L 2022).
Supplementary video available at https://youtu.be/q_XfjUDO39
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications,
such as road detection or scene understanding. In this paper, we present
convolutional patch networks, which are convolutional networks learned to
distinguish different image patches and which can be used for pixel-wise
labeling. We also show how to incorporate spatial information of the patch as
an input to the network, which allows for learning spatial priors for certain
categories jointly with an appearance model. In particular, we focus on road
detection and urban scene understanding, two application areas where we are
able to achieve state-of-the-art results on the KITTI as well as on the
LabelMeFacade dataset.
Furthermore, our paper offers a guideline for people working in the area and
desperately wandering through all the painstaking details that render training
CNs on image patches extremely difficult.Comment: VISAPP 2015 pape
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation
As the demand for enabling high-level autonomous driving has increased in
recent years and visual perception is one of the critical features to enable
fully autonomous driving, in this paper, we introduce an efficient approach for
simultaneous object detection, depth estimation and pixel-level semantic
segmentation using a shared convolutional architecture. The proposed network
model, which we named Driving Scene Perception Network (DSPNet), uses
multi-level feature maps and multi-task learning to improve the accuracy and
efficiency of object detection, depth estimation and image segmentation tasks
from a single input image. Hence, the resulting network model uses less than
850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a
1024x512 input image, and both precision and efficiency have been improved over
combination of single tasks.Comment: 9 pages, 7 figures, WACV'1
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for
a wide range of applications. Object detection has benefited enormously from
large-scale datasets, especially in the context of deep learning. For semantic
urban scene understanding, however, no current dataset adequately captures the
complexity of real-world urban scenes.
To address this, we introduce Cityscapes, a benchmark suite and large-scale
dataset to train and test approaches for pixel-level and instance-level
semantic labeling. Cityscapes is comprised of a large, diverse set of stereo
video sequences recorded in streets from 50 different cities. 5000 of these
images have high quality pixel-level annotations; 20000 additional images have
coarse annotations to enable methods that leverage large volumes of
weakly-labeled data. Crucially, our effort exceeds previous attempts in terms
of dataset size, annotation richness, scene variability, and complexity. Our
accompanying empirical study provides an in-depth analysis of the dataset
characteristics, as well as a performance evaluation of several
state-of-the-art approaches based on our benchmark.Comment: Includes supplemental materia
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
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