12 research outputs found
Joint Image and 3D Shape Part Representation in Large Collections for Object Blending
We propose a new approach to perform object shape retrieval from images, it can handle the
shape of the part of the object and combine parts from different sources to find a different 3D shape. Our
method creates a common representation for images and 3D models that enables mixing elements from
both kinds of inputs. Our approach automatically extracts the desired part and its 3D shape from each source
without the need of annotations. There are many applications to combining parts from images and 3D models,
for example, performing smart online catalogue searches by selecting the parts that we are looking for from
images or 3D models and retrieve a 3D shape that has the desired arrangement of parts. Our approach is
capable of obtaining the shape of the parts of an object from an image in the wild, independently of the pose
of the object and without the need of annotations of any kind
Accurate and linear time pose estimation from points and lines
The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such
scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based
counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error
that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms,
the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or
only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft
Infrastructure-based Multi-Camera Calibration using Radial Projections
Multi-camera systems are an important sensor platform for intelligent systems
such as self-driving cars. Pattern-based calibration techniques can be used to
calibrate the intrinsics of the cameras individually. However, extrinsic
calibration of systems with little to no visual overlap between the cameras is
a challenge. Given the camera intrinsics, infrastucture-based calibration
techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM
or Structure-from-Motion. In this paper, we propose to fully calibrate a
multi-camera system from scratch using an infrastructure-based approach.
Assuming that the distortion is mainly radial, we introduce a two-stage
approach. We first estimate the camera-rig extrinsics up to a single unknown
translation component per camera. Next, we solve for both the intrinsic
parameters and the missing translation components. Extensive experiments on
multiple indoor and outdoor scenes with multiple multi-camera systems show that
our calibration method achieves high accuracy and robustness. In particular,
our approach is more robust than the naive approach of first estimating
intrinsic parameters and pose per camera before refining the extrinsic
parameters of the system. The implementation is available at
https://github.com/youkely/InfrasCal.Comment: ECCV 202
Fast optical source for quantum key distribution based on semiconductor optical amplifiers
A novel integrated optical source capable of emitting faint pulses with
different polarization states and with different intensity levels at 100 MHz
has been developed. The source relies on a single laser diode followed by four
semiconductor optical amplifiers and thin film polarizers, connected through a
fiber network. The use of a single laser ensures high level of
indistinguishability in time and spectrum of the pulses for the four different
polarizations and three different levels of intensity. The applicability of the
source is demonstrated in the lab through a free space quantum key distribution
experiment which makes use of the decoy state BB84 protocol. We achieved a
lower bound secure key rate of the order of 3.64 Mbps and a quantum bit error
ratio as low as while the lower bound secure key rate
became 187 bps for an equivalent attenuation of 35 dB. To our knowledge, this
is the fastest polarization encoded QKD system which has been reported so far.
The performance, reduced size, low power consumption and the fact that the
components used can be space qualified make the source particularly suitable
for secure satellite communication
Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
Localization in challenging, natural environments, such as forests or woodlands, is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this letter, we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from three-dimensional point clouds by comparing triplets (anchor, positive, and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored toward loop closure detection resulting in a small model that can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payloads, such as drones, quadrupeds, or Unmanned Ground Vehicles (UGVs)
SKD: keypoint detection for point clouds using saliency estimation
We present SKD, a novel keypoint detector that uses saliency to determine the best candidates from a point cloud for tasks such as registration and reconstruction. The approach can be applied to any differentiable deep learning descriptor by using the gradients of that descriptor with respect to the 3D position of the input points as a measure of their saliency. The saliency is combined with the original descriptor and context information in a neural network, which is trained to learn robust keypoint candidates. The key intuition behind this approach is that keypoints are not extracted solely as a result of the geometry surrounding a point, but also take into account the descriptor's response. The approach was evaluated on two large LIDAR datasets - the Oxford RobotCar dataset and the KITTI dataset, where we obtain up to 50% improvement over the state-of-the-art in both matchability and repeatability. When performing sparse matching with the keypoints computed by our method we achieve a higher inlier ratio and faster convergence
Nodulation and growth of common bean (Phaseolus vulgaris L.) cultivars in hydroponic culture and in the field
International audienc