12,256 research outputs found
Robust Dense Mapping for Large-Scale Dynamic Environments
We present a stereo-based dense mapping algorithm for large-scale dynamic
urban environments. In contrast to other existing methods, we simultaneously
reconstruct the static background, the moving objects, and the potentially
moving but currently stationary objects separately, which is desirable for
high-level mobile robotic tasks such as path planning in crowded environments.
We use both instance-aware semantic segmentation and sparse scene flow to
classify objects as either background, moving, or potentially moving, thereby
ensuring that the system is able to model objects with the potential to
transition from static to dynamic, such as parked cars. Given camera poses
estimated from visual odometry, both the background and the (potentially)
moving objects are reconstructed separately by fusing the depth maps computed
from the stereo input. In addition to visual odometry, sparse scene flow is
also used to estimate the 3D motions of the detected moving objects, in order
to reconstruct them accurately. A map pruning technique is further developed to
improve reconstruction accuracy and reduce memory consumption, leading to
increased scalability. We evaluate our system thoroughly on the well-known
KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz,
with the primary bottleneck being the instance-aware semantic segmentation,
which is a limitation we hope to address in future work. The source code is
available from the project website (http://andreibarsan.github.io/dynslam).Comment: Presented at IEEE International Conference on Robotics and Automation
(ICRA), 201
Reconstruction of 3D Urban Scenes Using a Moving Lidar Sensor
In this report, we propose algorithms which interpret and display 3D environments.The input of this procedure is a LiDAR sensor mounted atop of a car. The sensor outputs a data stream covering more than 100 meters radius of space, collecting data at 15Hz. The recording is done in real environment on the streets of Budapest in real time, while the processing is offline, implemented on CPU keeping in mind the future implementation on GPUs to reach real time data processing. The aim is to segment several region classes (such as roads, building walls, vegetation) and to identify specified objects (such as people, vehicles, traffic signs) in the point clouds through a presegmentation step. To achieve this classification, we need several features such as the color and geometrical properties of the specified objects and their possible geometrical and physical interactions. Also, we need to take into account the time domain features calculated based on the LiDAR data stream. After this presegmentation step we are able to reconstruct building facades in 3D and to track the detected objects in the 3D space. Also, in the future, this processed data set can be registered against 2D images provided by conventional cameras to reproduce realistic, colored 3D virtua
MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond
Neural radiance fields (NeRF) and its subsequent variants have led to
remarkable progress in neural rendering. While most of recent neural rendering
works focus on objects and small-scale scenes, developing neural rendering
methods for city-scale scenes is of great potential in many real-world
applications. However, this line of research is impeded by the absence of a
comprehensive and high-quality dataset, yet collecting such a dataset over real
city-scale scenes is costly, sensitive, and technically difficult. To this end,
we build a large-scale, comprehensive, and high-quality synthetic dataset for
city-scale neural rendering researches. Leveraging the Unreal Engine 5 City
Sample project, we develop a pipeline to easily collect aerial and street city
views, accompanied by ground-truth camera poses and a range of additional data
modalities. Flexible controls over environmental factors like light, weather,
human and car crowd are also available in our pipeline, supporting the need of
various tasks covering city-scale neural rendering and beyond. The resulting
pilot dataset, MatrixCity, contains 67k aerial images and 452k street images
from two city maps of total size . On top of MatrixCity, a thorough
benchmark is also conducted, which not only reveals unique challenges of the
task of city-scale neural rendering, but also highlights potential improvements
for future works. The dataset and code will be publicly available at our
project page: https://city-super.github.io/matrixcity/.Comment: Accepted to ICCV 2023. Project page:
$\href{https://city-super.github.io/matrixcity/}{this\, https\, URL}
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