9,102 research outputs found
Detection-by-Localization: Maintenance-Free Change Object Detector
Recent researches demonstrate that self-localization performance is a very
useful measure of likelihood-of-change (LoC) for change detection. In this
paper, this "detection-by-localization" scheme is studied in a novel
generalized task of object-level change detection. In our framework, a given
query image is segmented into object-level subimages (termed "scene parts"),
which are then converted to subimage-level pixel-wise LoC maps via the
detection-by-localization scheme. Our approach models a self-localization
system as a ranking function, outputting a ranked list of reference images,
without requiring relevance score. Thanks to this new setting, we can
generalize our approach to a broad class of self-localization systems. Our
ranking based self-localization model allows to fuse self-localization results
from different modalities via an unsupervised rank fusion derived from a field
of multi-modal information retrieval (MMR).Comment: 7 pages, 3 figures, Technical repor
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
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