6 research outputs found
SuperPoint: Self-Supervised Interest Point Detection and Description
This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level interest point locations and associated descriptors in one forward
pass. We introduce Homographic Adaptation, a multi-scale, multi-homography
approach for boosting interest point detection repeatability and performing
cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on
the MS-COCO generic image dataset using Homographic Adaptation, is able to
repeatedly detect a much richer set of interest points than the initial
pre-adapted deep model and any other traditional corner detector. The final
system gives rise to state-of-the-art homography estimation results on HPatches
when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM
Workshop (DL4VSLAM2018
Theseus: A Library for Differentiable Nonlinear Optimization
We present Theseus, an efficient application-agnostic open source library for
differentiable nonlinear least squares (DNLS) optimization built on PyTorch,
providing a common framework for end-to-end structured learning in robotics and
vision. Existing DNLS implementations are application specific and do not
always incorporate many ingredients important for efficiency. Theseus is
application-agnostic, as we illustrate with several example applications that
are built using the same underlying differentiable components, such as
second-order optimizers, standard costs functions, and Lie groups. For
efficiency, Theseus incorporates support for sparse solvers, automatic
vectorization, batching, GPU acceleration, and gradient computation with
implicit differentiation and direct loss minimization. We do extensive
performance evaluation in a set of applications, demonstrating significant
efficiency gains and better scalability when these features are incorporated.
Project page: https://sites.google.com/view/theseus-a