53,719 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
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
Deep neural network (DNN) architectures have been shown to outperform
traditional pipelines for object segmentation and pose estimation using RGBD
data, but the performance of these DNN pipelines is directly tied to how
representative the training data is of the true data. Hence a key requirement
for employing these methods in practice is to have a large set of labeled data
for your specific robotic manipulation task, a requirement that is not
generally satisfied by existing datasets. In this paper we develop a pipeline
to rapidly generate high quality RGBD data with pixelwise labels and object
poses. We use an RGBD camera to collect video of a scene from multiple
viewpoints and leverage existing reconstruction techniques to produce a 3D
dense reconstruction. We label the 3D reconstruction using a human assisted
ICP-fitting of object meshes. By reprojecting the results of labeling the 3D
scene we can produce labels for each RGBD image of the scene. This pipeline
enabled us to collect over 1,000,000 labeled object instances in just a few
days. We use this dataset to answer questions related to how much training data
is required, and of what quality the data must be, to achieve high performance
from a DNN architecture
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
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