13,446 research outputs found
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
Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition
When a human drives a car along a road for the first time, they later
recognize where they are on the return journey typically without needing to
look in their rear-view mirror or turn around to look back, despite significant
viewpoint and appearance change. Such navigation capabilities are typically
attributed to our semantic visual understanding of the environment [1] beyond
geometry to recognizing the types of places we are passing through such as
"passing a shop on the left" or "moving through a forested area". Humans are in
effect using place categorization [2] to perform specific place recognition
even when the viewpoint is 180 degrees reversed. Recent advances in deep neural
networks have enabled high-performance semantic understanding of visual places
and scenes, opening up the possibility of emulating what humans do. In this
work, we develop a novel methodology for using the semantics-aware higher-order
layers of deep neural networks for recognizing specific places from within a
reference database. To further improve the robustness to appearance change, we
develop a descriptor normalization scheme that builds on the success of
normalization schemes for pure appearance-based techniques such as SeqSLAM [3].
Using two different datasets - one road-based, one pedestrian-based, we
evaluate the performance of the system in performing place recognition on
reverse traversals of a route with a limited field of view camera and no
turn-back-and-look behaviours, and compare to existing state-of-the-art
techniques and vanilla off-the-shelf features. The results demonstrate
significant improvements over the existing state of the art, especially for
extreme perceptual challenges that involve both great viewpoint change and
environmental appearance change. We also provide experimental analyses of the
contributions of the various system components.Comment: 9 pages, 11 figures, ICRA 201
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
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