30,152 research outputs found
Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery
Detection of buildings and other objects from aerial images has various
applications in urban planning and map making. Automated building detection
from aerial imagery is a challenging task, as it is prone to varying lighting
conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are
robust against some of these variations, although they fail to distinguish easy
and difficult examples. We train a detection algorithm from RGB-D images to
obtain a segmented mask by using the CNN architecture DenseNet.First, we
improve the performance of the model by applying a statistical re-sampling
technique called Bootstrapping and demonstrate that more informative examples
are retained. Second, the proposed method outperforms the non-bootstrapped
version by utilizing only one-sixth of the original training data and it
obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and
Spatial Information Science
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
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Exploiting synthetic data to learn deep models has attracted increasing
attention in recent years. However, the intrinsic domain difference between
synthetic and real images usually causes a significant performance drop when
applying the learned model to real world scenarios. This is mainly due to two
reasons: 1) the model overfits to synthetic images, making the convolutional
filters incompetent to extract informative representation for real images; 2)
there is a distribution difference between synthetic and real data, which is
also known as the domain adaptation problem. To this end, we propose a new
reality oriented adaptation approach for urban scene semantic segmentation by
learning from synthetic data. First, we propose a target guided distillation
approach to learn the real image style, which is achieved by training the
segmentation model to imitate a pretrained real style model using real images.
Second, we further take advantage of the intrinsic spatial structure presented
in urban scene images, and propose a spatial-aware adaptation scheme to
effectively align the distribution of two domains. These two modules can be
readily integrated with existing state-of-the-art semantic segmentation
networks to improve their generalizability when adapting from synthetic to real
urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting
from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness
of our method.Comment: Add experiments on SYNTHIA, CVPR 2018 camera-ready versio
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Localization in challenging, natural environments such as forests or
woodlands is an important capability for many applications from guiding a robot
navigating along a forest trail to monitoring vegetation growth with handheld
sensors. In this work we explore laser-based localization in both urban and
natural environments, which is suitable for online applications. We propose a
deep learning approach capable of learning meaningful descriptors directly from
3D point clouds by comparing triplets (anchor, positive and negative examples).
The approach learns a feature space representation for a set of segmented point
clouds that are matched between a current and previous observations. Our
learning method is tailored towards loop closure detection resulting in a small
model which can be deployed using only a CPU. The proposed learning method
would allow the full pipeline to run on robots with limited computational
payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info:
https://ori.ox.ac.uk/esm-localizatio
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