45 research outputs found
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
This paper presents a novel method for fully automatic and convenient
extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally
printed chessboard. The proposed method is based on the 3D corner estimation of
the chessboard from the sparse point cloud generated by one frame scan of the
LiDAR. To estimate the corners, we formulate a full-scale model of the
chessboard and fit it to the segmented 3D points of the chessboard. The model
is fitted by optimizing the cost function under constraints of correlation
between the reflectance intensity of laser and the color of the chessboard's
patterns. Powell's method is introduced for resolving the discontinuity problem
in optimization. The corners of the fitted model are considered as the 3D
corners of the chessboard. Once the corners of the chessboard in the 3D point
cloud are estimated, the extrinsic calibration of the two sensors is converted
to a 3D-2D matching problem. The corresponding 3D-2D points are used to
calculate the absolute pose of the two sensors with Unified Perspective-n-Point
(UPnP). Further, the calculated parameters are regarded as initial values and
are refined using the Levenberg-Marquardt method. The performance of the
proposed corner detection method from the 3D point cloud is evaluated using
simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR
and a Ladybug3 camera under the proposed re-projection error metric,
qualitatively and quantitatively demonstrate the accuracy and stability of the
final extrinsic calibration parameters.Comment: 20 pages, submitted to the journal of Remote Sensin
Weakly Supervised Silhouette-based Semantic Scene Change Detection
This paper presents a novel semantic scene change detection scheme with only
weak supervision. A straightforward approach for this task is to train a
semantic change detection network directly from a large-scale dataset in an
end-to-end manner. However, a specific dataset for this task, which is usually
labor-intensive and time-consuming, becomes indispensable. To avoid this
problem, we propose to train this kind of network from existing datasets by
dividing this task into change detection and semantic extraction. On the other
hand, the difference in camera viewpoints, for example, images of the same
scene captured from a vehicle-mounted camera at different time points, usually
brings a challenge to the change detection task. To address this challenge, we
propose a new siamese network structure with the introduction of correlation
layer. In addition, we create a publicly available dataset for semantic change
detection to evaluate the proposed method. The experimental results verified
both the robustness to viewpoint difference in change detection task and the
effectiveness for semantic change detection of the proposed networks. Our code
and dataset are available at https://github.com/xdspacelab/sscdnet.Comment: Accepted at the 2020 IEEE International Conference on Robotics and
Automation (ICRA). Code and dataset are available at
https://github.com/xdspacelab/sscdne
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
In this paper, we propose a method for cloud removal from visible light RGB
satellite images by extending the conditional Generative Adversarial Networks
(cGANs) from RGB images to multispectral images. Satellite images have been
widely utilized for various purposes, such as natural environment monitoring
(pollution, forest or rivers), transportation improvement and prompt emergency
response to disasters. However, the obscurity caused by clouds makes it
unstable to monitor the situation on the ground with the visible light camera.
Images captured by a longer wavelength are introduced to reduce the effects of
clouds. Synthetic Aperture Radar (SAR) is such an example that improves
visibility even the clouds exist. On the other hand, the spatial resolution
decreases as the wavelength increases. Furthermore, the images captured by long
wavelengths differs considerably from those captured by visible light in terms
of their appearance. Therefore, we propose a network that can remove clouds and
generate visible light images from the multispectral images taken as inputs.
This is achieved by extending the input channels of cGANs to be compatible with
multispectral images. The networks are trained to output images that are close
to the ground truth using the images synthesized with clouds over the ground
truth as inputs. In the available dataset, the proportion of images of the
forest or the sea is very high, which will introduce bias in the training
dataset if uniformly sampled from the original dataset. Thus, we utilize the
t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of
bias in the training dataset. Finally, we confirm the feasibility of the
proposed network on the dataset of four bands images, which include three
visible light bands and one near-infrared (NIR) band
Hierarchical Neural Memory Network for Low Latency Event Processing
This paper proposes a low latency neural network architecture for event-based
dense prediction tasks. Conventional architectures encode entire scene contents
at a fixed rate regardless of their temporal characteristics. Instead, the
proposed network encodes contents at a proper temporal scale depending on its
movement speed. We achieve this by constructing temporal hierarchy using
stacked latent memories that operate at different rates. Given low latency
event steams, the multi-level memories gradually extract dynamic to static
scene contents by propagating information from the fast to the slow memory
modules. The architecture not only reduces the redundancy of conventional
architectures but also exploits long-term dependencies. Furthermore, an
attention-based event representation efficiently encodes sparse event streams
into the memory cells. We conduct extensive evaluations on three event-based
dense prediction tasks, where the proposed approach outperforms the existing
methods on accuracy and latency, while demonstrating effective event and image
fusion capabilities. The code is available at https://hamarh.github.io/hmnet/Comment: Accepted to CVPR 202