6,999 research outputs found
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fĂĽl alatt kell rákeresni a "Stereo Vision" cĂmre Ă©s az 1. fejezetre
Rate-Distortion Efficient Piecewise Planar 3D Scene Representation from 2-D Images
Cataloged from PDF version of article.In any practical application of the 2-D-to-3-D conversion
that involves storage and transmission, representation effi-
ciency has an undisputable importance that is not reflected in the
attention the topic received. In order to address this problem, a
novel algorithm, which yields efficient 3-D representations in the
rate distortion sense, is proposed. The algorithm utilizes two views
of a scene to build a mesh-based representation incrementally, via
adding new vertices, while minimizing a distortion measure. The
experimental results indicate that, in scenes that can be approximated
by planes, the proposed algorithm is superior to the dense
depth map and, in some practical situations, to the block motion
vector-based representations in the rate-distortion sense
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