4,518 research outputs found
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
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data
While the majority of today's object class models provide only 2D bounding
boxes, far richer output hypotheses are desirable including viewpoint,
fine-grained category, and 3D geometry estimate. However, models trained to
provide richer output require larger amounts of training data, preferably well
covering the relevant aspects such as viewpoint and fine-grained categories. In
this paper, we address this issue from the perspective of transfer learning,
and design an object class model that explicitly leverages correlations between
visual features. Specifically, our model represents prior distributions over
permissible multi-view detectors in a parametric way -- the priors are learned
once from training data of a source object class, and can later be used to
facilitate the learning of a detector for a target class. As we show in our
experiments, this transfer is not only beneficial for detectors based on
basic-level category representations, but also enables the robust learning of
detectors that represent classes at finer levels of granularity, where training
data is typically even scarcer and more unbalanced. As a result, we report
largely improved performance in simultaneous 2D object localization and
viewpoint estimation on a recent dataset of challenging street scenes.Comment: 13 pages, 7 figures, 4 tables, International Conference on Learning
Representations 201
Towards Efficient Neural Scene Graphs by Learning Consistency Fields
Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from
novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it
to dynamic scenes (video) with multiple objects. Nevertheless, computationally
heavy ray marching for every image frame becomes a huge burden. In this paper,
taking advantage of significant redundancy across adjacent frames in videos, we
propose a feature-reusing framework. From the first try of naively reusing the
NSG features, however, we learn that it is crucial to disentangle
object-intrinsic properties consistent across frames from transient ones. Our
proposed method, \textit{Consistency-Field-based NSG (CF-NSG)}, reformulates
neural radiance fields to additionally consider \textit{consistency fields}.
With disentangled representations, CF-NSG takes full advantage of the
feature-reusing scheme and performs an extended degree of scene manipulation in
a more controllable manner. We empirically verify that CF-NSG greatly improves
the inference efficiency by using 85\% less queries than NSG without notable
degradation in rendering quality. Code will be available at:
https://github.com/ldynx/CF-NSGComment: BMVC 2022, 22 page
Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision
In order to improve usability and safety, modern unmanned aerial vehicles
(UAVs) are equipped with sensors to monitor the environment, such as
laser-scanners and cameras. One important aspect in this monitoring process is
to detect obstacles in the flight path in order to avoid collisions. Since a
large number of consumer UAVs suffer from tight weight and power constraints,
our work focuses on obstacle avoidance based on a lightweight stereo camera
setup. We use disparity maps, which are computed from the camera images, to
locate obstacles and to automatically steer the UAV around them. For disparity
map computation we optimize the well-known semi-global matching (SGM) approach
for the deployment on an embedded FPGA. The disparity maps are then converted
into simpler representations, the so called U-/V-Maps, which are used for
obstacle detection. Obstacle avoidance is based on a reactive approach which
finds the shortest path around the obstacles as soon as they have a critical
distance to the UAV. One of the fundamental goals of our work was the reduction
of development costs by closing the gap between application development and
hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for
porting our algorithms, which are written in C/C++, to the embedded FPGA. We
evaluated our implementation of the disparity estimation on the KITTI Stereo
2015 benchmark. The integrity of the overall realtime reactive obstacle
avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in
conjunction with two flight simulators.Comment: Accepted in the International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Scienc
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