3,274 research outputs found
Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and Densenets can improve performance on Cifar-10+ consistently, while
having additional pleasant properties like steerability. By exploiting these
transformation properties explicitly, we arrive at dynamic steerable blocks.
They are an extension of residual blocks, that are able to seamlessly transform
filters under pre-defined transformations, conditioned on the input at training
and inference time. Dynamic steerable blocks learn the degree of invariance
from data and locally adapt filters, allowing them to apply a different
geometrical variant of the same filter to each location of the feature map.
When evaluated on the Berkeley Segmentation contour detection dataset, our
approach outperforms all competing approaches that do not utilize pre-training.
Our results highlight the benefits of image-based regularization to deep
networks
FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation
We focus on the problem of LiDAR point cloud based loop detection (or
Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA)
techniques directly generate learned embeddings of a given point cloud, require
large data transfers, and are not robust to wide variations in 6
Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong priors in an
unstructured point cloud leads to highly inaccurate LDC. In this original
approach, we propose independent roll and pitch canonicalization of the point
clouds using a common dominant ground plane. Discretization of the
canonicalized point cloud along the axis perpendicular to the ground plane
leads to an image similar to Digital Elevation Maps (DEMs), which exposes
strong spatial priors in the scene. Our experiments show that LDC based on
learnt embeddings of such DEMs is not only data efficient but also
significantly more robust, and generalizable than the current SOTA. We report
significant performance gain in terms of Average Precision for loop detection
and absolute translation/rotation error for relative pose estimation (or loop
closure) on Kitti, GPR and Oxford Robot Car over multiple SOTA LDC methods. Our
encoder technique allows to compress the original point cloud by over 830
times. To further test the robustness of our technique we create and opensource
a custom dataset called Lidar-UrbanFly Dataset (LUF) which consists of point
clouds obtained from a LiDAR mounted on a quadrotor
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations
Biometric Authentication System on Mobile Personal Devices
We propose a secure, robust, and low-cost biometric authentication system on the mobile personal device for the personal network. The system consists of the following five key modules: 1) face detection; 2) face registration; 3) illumination normalization; 4) face verification; and 5) information fusion. For the complicated face authentication task on the devices with limited resources, the emphasis is largely on the reliability and applicability of the system. Both theoretical and practical considerations are taken. The final system is able to achieve an equal error rate of 2% under challenging testing protocols. The low hardware and software cost makes the system well adaptable to a large range of security applications
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