Structure from Recurrent Motion: From Rigidity to Recurrency

This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM) from a long monocular video sequence observing a non-rigid object performing recurrent and possibly repetitive dynamic action. Departing from the traditional idea of using linear low-order or lowrank shape model for the task of NRSfM, our method exploits the property of shape recurrency (i.e., many deforming shapes tend to repeat themselves in time). We show that recurrency is in fact a generalized rigidity. Based on this, we reduce NRSfM problems to rigid ones provided that certain recurrency condition is satisfied. Given such a reduction, standard rigid-SfM techniques are directly applicable (without any change) to the reconstruction of non-rigid dynamic shapes. To implement this idea as a practical approach, this paper develops efficient algorithms for automatic recurrency detection, as well as camera view clustering via a rigidity-check. Experiments on both simulated sequences and real data demonstrate the effectiveness of the method. Since this paper offers a novel perspective on rethinking structure-from-motion, we hope it will inspire other new problems in the field.Comment: To appear in CVPR 201

Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Automatic discovery of category-specific 3D keypoints from a collection of objects of a category is a challenging problem. The difficulty is added when objects are represented by 3D point clouds, with variations in shape and semantic parts and unknown coordinate frames. We define keypoints to be category-specific, if they meaningfully represent objects’ shape and their correspondences can be simply established order-wise across all objects. This paper aims at learning such 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category. In order to do so, we model shapes defined by the keypoints, within a category, using the symmetric linear basis shapes without assuming the plane of symmetry to be known. The usage of symmetry prior leads us to learn stable keypoints suitable for higher misalignments. To the best of our knowledge, this is the first work on learning such keypoints directly from 3D point clouds for a general category. Using objects from four benchmark datasets, we demonstrate the quality of our learned keypoints by quantitative and qualitative evaluations. Our experiments also show that the keypoints discovered by our method are geometrically and semantically consistent

PEER Testbed Study on a Laboratory Building: Exercising Seismic Performance Assessment

From 2002 to 2004 (years five and six of a ten-year funding cycle), the PEER Center organized the majority of its research around six testbeds. Two buildings and two bridges, a campus, and a transportation network were selected as case studies to “exercise” the PEER performance-based earthquake engineering methodology. All projects involved interdisciplinary teams of researchers, each producing data to be used by other colleagues in their research. The testbeds demonstrated that it is possible to create the data necessary to populate the PEER performancebased framing equation, linking the hazard analysis, the structural analysis, the development of damage measures, loss analysis, and decision variables. This report describes one of the building testbeds—the UC Science Building. The project was chosen to focus attention on the consequences of losses of laboratory contents, particularly downtime. The UC Science testbed evaluated the earthquake hazard and the structural performance of a well-designed recently built reinforced concrete laboratory building using the OpenSees platform. Researchers conducted shake table tests on samples of critical laboratory contents in order to develop fragility curves used to analyze the probability of losses based on equipment failure. The UC Science testbed undertook an extreme case in performance assessment—linking performance of contents to operational failure. The research shows the interdependence of building structure, systems, and contents in performance assessment, and highlights where further research is needed. The Executive Summary provides a short description of the overall testbed research program, while the main body of the report includes summary chapters from individual researchers. More extensive research reports are cited in the reference section of each chapter