2,089 research outputs found
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization
In this paper, we propose an end-to-end framework that jointly learns
keypoint detection, descriptor representation and cross-frame matching for the
task of image-based 3D localization. Prior art has tackled each of these
components individually, purportedly aiming to alleviate difficulties in
effectively train a holistic network. We design a self-supervised image warping
correspondence loss for both feature detection and matching, a
weakly-supervised epipolar constraints loss on relative camera pose learning,
and a directional matching scheme that detects key-point features in a source
image and performs coarse-to-fine correspondence search on the target image. We
leverage this framework to enforce cycle consistency in our matching module. In
addition, we propose a new loss to robustly handle both definite inlier/outlier
matches and less-certain matches. The integration of these learning mechanisms
enables end-to-end training of a single network performing all three
localization components. Bench-marking our approach on public data-sets,
exemplifies how such an end-to-end framework is able to yield more accurate
localization that out-performs both traditional methods as well as
state-of-the-art weakly supervised methods
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