Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5

Atomic Hydrogen Cleaning of Polarized GaAs Photocathodes

Atomic hydrogen cleaning followed by heat cleaning at 450$^\circ$C was used to prepare negative-electron-affinity GaAs photocathodes. When hydrogen ions were eliminated, quantum efficiencies of 15% were obtained for bulk GaAs cathodes, higher than the results obtained using conventional 600$^\circ$C heat cleaning. The low-temperature cleaning technique was successfully applied to thin, strained GaAs cathodes used for producing highly polarized electrons. No depolarization was observed even when the optimum cleaning time of about 30 seconds was extended by a factor of 100

The SLAC Polarized Electron Source

The SLAC PES, developed in the early 1990s for the SLC, has been in continuous use since 1992, during which time it has undergone numerous upgrades. The upgrades include improved cathodes with their matching laser systems, modified activation techniques and better diagnostics. The source itself and its performance with these upgrades will be described with special attention given to recent high-intensity long-pulse operation for the E-158 fixed-target parity-violating experiment.Comment: 6 pages, 4 figures, Workshop on Polarized Electron Sources and Polarimeters (PESP 2002), September 4-6, 2002, Danvers, M

Hydrodynamic Models for Heavy-Ion Collisions, and beyond

A generic property of a first-order phase transition in equilibrium, and in the limit of large entropy per unit of conserved charge, is the smallness of the isentropic speed of sound in the ``mixed phase''. A specific prediction is that this should lead to a non-isotropic momentum distribution of nucleons in the reaction plane (for energies around 40 AGeV in our model calculation). On the other hand, we show that from present effective theories for low-energy QCD one does not expect the thermal transition rate between various states of the effective potential to be much larger than the expansion rate, questioning the applicability of the idealized Maxwell/Gibbs construction. Experimental data could soon provide essential information on the dynamics of the phase transition.Comment: 10 Pages, 4 Figures. Presented at 241st WE-Heraeus Seminar: Symposium on Fundamental Issues in Elementary Matter: In Honor and Memory of Michael Danos, Bad Honnef, Germany, 25-29 Sep 200

Two-View Geometry Scoring Without Correspondences

Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded as a reliable indicator of 'consensus'. We examine this scoring heuristic, and find that it favors disappointing models under certain circumstances. As a remedy, we propose the Fundamental Scoring Network (FSNet), which infers a score for a pair of overlap-ping images and any proposed fundamental matrix. It does not rely on sparse correspondences, but rather embodies a two-view geometry model through an epipolar attention mechanism that predicts the pose error of the two images. FSNet can be incorporated into traditional RANSAC loops. We evaluate FSNet onfundamental and essential matrix estimation on indoor and outdoor datasets, and establish that FSNet can successfully identify good poses for pairs of images with few or unreliable correspondences. Besides, we show that naively combining FSNet with MAGSAC++ scoring approach achieves state of the art results

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects

We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Evolution of Baryon-Free Matter Produced in Relativistic Heavy-Ion Collisions

A 3-fluid hydrodynamic model is introduced for simulating heavy-ion collisions at incident energies between few and about 200 AGeV. In addition to the two baryon-rich fluids of 2-fluid models, the new model incorporates a third, baryon-free (i.e. with zero net baryonic charge) fluid which is created in the mid-rapidity region. Its evolution is delayed due to a formation time $\tau$, during which the baryon-free fluid neither thermalizes nor interacts with the baryon-rich fluids. After formation it thermalizes and starts to interact with the baryon-rich fluids. It is found that for $\tau$=0 the interaction strongly affects the baryon-free fluid. However, at reasonable finite formation time, $\tau$=1 fm/c, the effect of this interaction turns out to be substantially reduced although still noticeable. Baryonic observables are only slightly affected by the interaction with the baryon-free fluid.Comment: 17 pages, 3 figures, submitted to the issue of Phys. of Atomic Nuclei dedicated to S.T. Belyaev on the occasion of his 80th birthday, typos correcte

Microscopic description of anisotropic flow in relativistic heavy ion collisions

Anisotropic flow of hadrons is studied in heavy ion collisions at SPS and RHIC energies within the microscopic quark-gluon string model. The model was found to reproduce correctly many of the flow features, e.g., the wiggle structure of direct flow of nucleons at midrapidity, or centrality, rapidity, and transverse momentum dependences of elliptic flow. Further predictions are made. The differences in the development of the anisotropic flow components are linked to the freeze-out conditions, which are quite different for baryons and mesons.Comment: Proceedings of the Erice School on Nuclear Physics (Erice, Italy, September 16-24, 2003