17 research outputs found
Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution
We propose a robust and fast bundle adjustment solution that estimates the
6-DoF pose of the camera and the geometry of the environment based on
measurements from a rolling shutter (RS) camera. This tackles the challenges in
the existing works, namely relying on additional sensors, high frame rate video
as input, restrictive assumptions on camera motion, readout direction, and poor
efficiency. To this end, we first investigate the influence of normalization to
the image point on RSBA performance and show its better approximation in
modelling the real 6-DoF camera motion. Then we present a novel analytical
model for the visual residual covariance, which can be used to standardize the
reprojection error during the optimization, consequently improving the overall
accuracy. More importantly, the combination of normalization and covariance
standardization weighting in RSBA (NW-RSBA) can avoid common planar degeneracy
without needing to constrain the filming manner. Besides, we propose an
acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix
and Schur complement. The extensive synthetic and real data experiments verify
the effectiveness and efficiency of the proposed solution over the
state-of-the-art works. We also demonstrate the proposed method can be easily
implemented and plug-in famous GSSfM and GSSLAM systems as completed RSSfM and
RSSLAM solutions
Towards High-Frequency Tracking and Fast Edge-Aware Optimization
This dissertation advances the state of the art for AR/VR tracking systems by
increasing the tracking frequency by orders of magnitude and proposes an
efficient algorithm for the problem of edge-aware optimization.
AR/VR is a natural way of interacting with computers, where the physical and
digital worlds coexist. We are on the cusp of a radical change in how humans
perform and interact with computing. Humans are sensitive to small
misalignments between the real and the virtual world, and tracking at
kilo-Hertz frequencies becomes essential. Current vision-based systems fall
short, as their tracking frequency is implicitly limited by the frame-rate of
the camera. This thesis presents a prototype system which can track at orders
of magnitude higher than the state-of-the-art methods using multiple commodity
cameras. The proposed system exploits characteristics of the camera
traditionally considered as flaws, namely rolling shutter and radial
distortion. The experimental evaluation shows the effectiveness of the method
for various degrees of motion.
Furthermore, edge-aware optimization is an indispensable tool in the computer
vision arsenal for accurate filtering of depth-data and image-based rendering,
which is increasingly being used for content creation and geometry processing
for AR/VR. As applications increasingly demand higher resolution and speed,
there exists a need to develop methods that scale accordingly. This
dissertation proposes such an edge-aware optimization framework which is
efficient, accurate, and algorithmically scales well, all of which are much
desirable traits not found jointly in the state of the art. The experiments
show the effectiveness of the framework in a multitude of computer vision tasks
such as computational photography and stereo.Comment: PhD thesi
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
Neural Radiance Fields (NeRF) has received much attention recently due to its
impressive capability to represent 3D scene and synthesize novel view images.
Existing works usually assume that the input images are captured by a global
shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied
to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter
effect would also affect the accuracy of the camera pose estimation (e.g. via
COLMAP), which further prevents the success of NeRF algorithm with RS images.
In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance
Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and
recover accurate camera motion trajectory simultaneously under the framework of
NeRF, by modeling the physical image formation process of a RS camera.
Experimental results demonstrate that USB-NeRF achieves better performance
compared to prior works, in terms of RS effect removal, novel view image
synthesis as well as camera motion estimation. Furthermore, our algorithm can
also be used to recover high-fidelity high frame-rate global shutter video from
a sequence of RS images
Direct Sparse Odometry with Rolling Shutter
Neglecting the effects of rolling-shutter cameras for visual odometry (VO)
severely degrades accuracy and robustness. In this paper, we propose a novel
direct monocular VO method that incorporates a rolling-shutter model. Our
approach extends direct sparse odometry which performs direct bundle adjustment
of a set of recent keyframe poses and the depths of a sparse set of image
points. We estimate the velocity at each keyframe and impose a
constant-velocity prior for the optimization. In this way, we obtain a near
real-time, accurate direct VO method. Our approach achieves improved results on
challenging rolling-shutter sequences over state-of-the-art global-shutter VO
撮像系の物理モデルに基づく動画像からの3次元復元手法の高精度化に関する研究
Tohoku University岡谷貴之課
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved