1,333 research outputs found
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
In this work, a deep learning approach has been developed to carry out road
detection by fusing LIDAR point clouds and camera images. An unstructured and
sparse point cloud is first projected onto the camera image plane and then
upsampled to obtain a set of dense 2D images encoding spatial information.
Several fully convolutional neural networks (FCNs) are then trained to carry
out road detection, either by using data from a single sensor, or by using
three fusion strategies: early, late, and the newly proposed cross fusion.
Whereas in the former two fusion approaches, the integration of multimodal
information is carried out at a predefined depth level, the cross fusion FCN is
designed to directly learn from data where to integrate information; this is
accomplished by using trainable cross connections between the LIDAR and the
camera processing branches.
To further highlight the benefits of using a multimodal system for road
detection, a data set consisting of visually challenging scenes was extracted
from driving sequences of the KITTI raw data set. It was then demonstrated
that, as expected, a purely camera-based FCN severely underperforms on this
data set. A multimodal system, on the other hand, is still able to provide high
accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI
road benchmark where it achieved excellent performance, with a MaxF score of
96.03%, ranking it among the top-performing approaches
A Joint 3D-2D based Method for Free Space Detection on Roads
In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Recent work has focused on generating synthetic imagery to increase the size
and variability of training data for learning visual tasks in urban scenes.
This includes increasing the occurrence of occlusions or varying environmental
and weather effects. However, few have addressed modeling variation in the
sensor domain. Sensor effects can degrade real images, limiting
generalizability of network performance on visual tasks trained on synthetic
data and tested in real environments. This paper proposes an efficient,
automatic, physically-based augmentation pipeline to vary sensor effects
--chromatic aberration, blur, exposure, noise, and color cast-- for synthetic
imagery. In particular, this paper illustrates that augmenting synthetic
training datasets with the proposed pipeline reduces the domain gap between
synthetic and real domains for the task of object detection in urban driving
scenes
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
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