23,458 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
A framework for realistic 3D tele-immersion
Meeting, socializing and conversing online with a group of people using teleconferencing systems is still quite differ- ent from the experience of meeting face to face. We are abruptly aware that we are online and that the people we are engaging with are not in close proximity. Analogous to how talking on the telephone does not replicate the experi- ence of talking in person. Several causes for these differences have been identified and we propose inspiring and innova- tive solutions to these hurdles in attempt to provide a more realistic, believable and engaging online conversational expe- rience. We present the distributed and scalable framework REVERIE that provides a balanced mix of these solutions. Applications build on top of the REVERIE framework will be able to provide interactive, immersive, photo-realistic ex- periences to a multitude of users that for them will feel much more similar to having face to face meetings than the expe- rience offered by conventional teleconferencing systems
Volumetric Framework for Third-Party Content Placement in Virtual 3D Environments
A content distribution system facilitates the placement of third-party content in virtual, three-dimensional (3D) environments. The third-party content may be shown on virtual, 3D objects. The system defines a technical and financial framework by which developers of 3D environments can monetize their environments by allotting space for the display of third-party virtual objects. In some aspects, the content distribution system determines an amount to charge a third-party content provider for placing their content on a virtual object in a 3D environment based on the volume of the virtual object
Smoke and Shadows: Rendering and Light Interaction of Smoke in Real-Time Rendered Virtual Environments
Realism in computer graphics depends upon digitally representing what we see in the world with careful attention to detail, which usually requires a high degree of complexity in modelling the scene. The inevitable trade-off between realism and performance means that new techniques that aim to improve the visual fidelity of a scene must do so without compromising the real-time rendering performance. We describe and discuss a simple method for realistically casting shadows from an opaque solid object through a GPU (graphics processing unit) based particle system representing natural phenomena, such as smoke
Constructing a gazebo: supporting teamwork in a tightly coupled, distributed task in virtual reality
Many tasks require teamwork. Team members may work concurrently, but there must be some occasions of coming together. Collaborative virtual environments (CVEs) allow distributed teams to come together across distance to share a task. Studies of CVE systems have tended to focus on the sense of presence or copresence with other people. They have avoided studying close interaction between us-ers, such as the shared manipulation of objects, because CVEs suffer from inherent network delays and often have cumbersome user interfaces. Little is known about the ef-fectiveness of collaboration in tasks requiring various forms of object sharing and, in particular, the concurrent manipu-lation of objects.
This paper investigates the effectiveness of supporting teamwork among a geographically distributed group in a task that requires the shared manipulation of objects. To complete the task, users must share objects through con-current manipulation of both the same and distinct at-tributes. The effectiveness of teamwork is measured in terms of time taken to achieve each step, as well as the impression of users. The effect of interface is examined by comparing various combinations of walk-in cubic immersive projection technology (IPT) displays and desktop devices
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data size as scene size increases. To this end, we devise a
fully-convolutional generative 3D CNN model whose filter kernels are invariant
to the overall scene size. The model can be trained on scene subvolumes but
deployed on arbitrarily large scenes at test time. In addition, we propose a
coarse-to-fine inference strategy in order to produce high-resolution output
while also leveraging large input context sizes. In an extensive series of
experiments, we carefully evaluate different model design choices, considering
both deterministic and probabilistic models for completion and semantic
inference. Our results show that we outperform other methods not only in the
size of the environments handled and processing efficiency, but also with
regard to completion quality and semantic segmentation performance by a
significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF
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