1,091 research outputs found
General Dynamic Scene Reconstruction from Multiple View Video
This paper introduces a general approach to dynamic scene reconstruction from
multiple moving cameras without prior knowledge or limiting constraints on the
scene structure, appearance, or illumination. Existing techniques for dynamic
scene reconstruction from multiple wide-baseline camera views primarily focus
on accurate reconstruction in controlled environments, where the cameras are
fixed and calibrated and background is known. These approaches are not robust
for general dynamic scenes captured with sparse moving cameras. Previous
approaches for outdoor dynamic scene reconstruction assume prior knowledge of
the static background appearance and structure. The primary contributions of
this paper are twofold: an automatic method for initial coarse dynamic scene
segmentation and reconstruction without prior knowledge of background
appearance or structure; and a general robust approach for joint segmentation
refinement and dense reconstruction of dynamic scenes from multiple
wide-baseline static or moving cameras. Evaluation is performed on a variety of
indoor and outdoor scenes with cluttered backgrounds and multiple dynamic
non-rigid objects such as people. Comparison with state-of-the-art approaches
demonstrates improved accuracy in both multiple view segmentation and dense
reconstruction. The proposed approach also eliminates the requirement for prior
knowledge of scene structure and appearance
Object-level 3D Semantic Mapping using a Network of Smart Edge Sensors
Autonomous robots that interact with their environment require a detailed
semantic scene model. For this, volumetric semantic maps are frequently used.
The scene understanding can further be improved by including object-level
information in the map. In this work, we extend a multi-view 3D semantic
mapping system consisting of a network of distributed smart edge sensors with
object-level information, to enable downstream tasks that need object-level
input. Objects are represented in the map via their 3D mesh model or as an
object-centric volumetric sub-map that can model arbitrary object geometry when
no detailed 3D model is available. We propose a keypoint-based approach to
estimate object poses via PnP and refinement via ICP alignment of the 3D object
model with the observed point cloud segments. Object instances are tracked to
integrate observations over time and to be robust against temporary occlusions.
Our method is evaluated on the public Behave dataset where it shows pose
estimation accuracy within a few centimeters and in real-world experiments with
the sensor network in a challenging lab environment where multiple chairs and a
table are tracked through the scene online, in real time even under high
occlusions.Comment: 9 pages, 12 figures, 6th IEEE International Conference on Robotic
Computing (IRC), Naples, Italy, December 202
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