2,361 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
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.Comment: In Proc. CVPR 201
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