526 research outputs found
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
Active depth cameras suffer from several limitations, which cause incomplete
and noisy depth maps, and may consequently affect the performance of RGB-D
Odometry. To address this issue, this paper presents a visual odometry method
based on point and line features that leverages both measurements from a depth
sensor and depth estimates from camera motion. Depth estimates are generated
continuously by a probabilistic depth estimation framework for both types of
features to compensate for the lack of depth measurements and inaccurate
feature depth associations. The framework models explicitly the uncertainty of
triangulating depth from both point and line observations to validate and
obtain precise estimates. Furthermore, depth measurements are exploited by
propagating them through a depth map registration module and using a
frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D
reprojection errors, independently. Results on RGB-D sequences captured on
large indoor and outdoor scenes, where depth sensor limitations are critical,
show that the combination of depth measurements and estimates through our
approach is able to overcome the absence and inaccuracy of depth measurements.Comment: IROS 201
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
Depth sensing is useful in a variety of applications that range from
augmented reality to robotics. Time-of-flight (TOF) cameras are appealing
because they obtain dense depth measurements with minimal latency. However, for
many battery-powered devices, the illumination source of a TOF camera is power
hungry and can limit the battery life of the device. To address this issue, we
present an algorithm that lowers the power for depth sensing by reducing the
usage of the TOF camera and estimating depth maps using concurrently collected
images. Our technique also adaptively controls the TOF camera and enables it
when an accurate depth map cannot be estimated. To ensure that the overall
system power for depth sensing is reduced, we design our algorithm to run on a
low power embedded platform, where it outputs 640x480 depth maps at 30 frames
per second. We evaluate our approach on several RGB-D datasets, where it
produces depth maps with an overall mean relative error of 0.96% and reduces
the usage of the TOF camera by 85%. When used with commercial TOF cameras, we
estimate that our algorithm can lower the total power for depth sensing by up
to 73%
ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays
High-frequency displays are gaining immense popularity because of their
increasing use in video games and virtual reality applications. However, the
issue is that the underlying GPUs cannot continuously generate frames at this
high rate -- this results in a less smooth and responsive experience.
Furthermore, if the frame rate is not synchronized with the refresh rate, the
user may experience screen tearing and stuttering. Previous works propose
increasing the frame rate to provide a smooth experience on modern displays by
predicting new frames based on past or future frames. Interpolation and
extrapolation are two widely used algorithms that predict new frames.
Interpolation requires waiting for the future frame to make a prediction, which
adds additional latency. On the other hand, extrapolation provides a better
quality of experience because it relies solely on past frames -- it does not
incur any additional latency. The simplest method to extrapolate a frame is to
warp the previous frame using motion vectors; however, the warped frame may
contain improperly rendered visual artifacts due to dynamic objects -- this
makes it very challenging to design such a scheme. Past work has used DNNs to
get good accuracy, however, these approaches are slow. This paper proposes
Exwarp -- an approach based on reinforcement learning (RL) to intelligently
choose between the slower DNN-based extrapolation and faster warping-based
methods to increase the frame rate by 4x with an almost negligible reduction in
the perceived image quality
LightGlue: Local Feature Matching at Light Speed
We introduce LightGlue, a deep neural network that learns to match local
features across images. We revisit multiple design decisions of SuperGlue, the
state of the art in sparse matching, and derive simple but effective
improvements. Cumulatively, they make LightGlue more efficient - in terms of
both memory and computation, more accurate, and much easier to train. One key
property is that LightGlue is adaptive to the difficulty of the problem: the
inference is much faster on image pairs that are intuitively easy to match, for
example because of a larger visual overlap or limited appearance change. This
opens up exciting prospects for deploying deep matchers in latency-sensitive
applications like 3D reconstruction. The code and trained models are publicly
available at https://github.com/cvg/LightGlue
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