Live 3D shape reconstruction, recognition and registration

We present a video-based system which interactively captures the geometry of a 3D object in the form of a point cloud, then recognizes and registers known objects in this point cloud in a matter of seconds (fig. 1). In order to achieve interactive speed, we exploit both efficient inference algorithms and parallel computation, often on a GPU. The system can be broken down into two distinct phases: geometry capture, and object inference. We now discuss these in further detail. © 2011 IEEE.

A new distance for scale-invariant 3D shape recognition and registration

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2011 IEEE.

Study of the turbulence in the air-side and water-side boundary layers in experimental laboratory wind induced surface waves

This study detailed the structure of turbulence in the air-side and water-side boundary layers in wind-induced surface waves. Inside the air boundary layer, the kurtosis is always greater than 3 (the value for normal distribution) for both horizontal and vertical velocity fluctuations. The skewness for the horizontal velocity is negative, but the skewness for the vertical velocity is always positive. On the water side, the kurtosis is always greater than 3, and the skewness is slightly negative for the horizontal velocity and slightly positive for the vertical velocity. The statistics of the angle between the instantaneous vertical fluctuation and the instantaneous horizontal velocity in the air is similar to those obtained over solid walls. Measurements in water show a large variance, and the peak is biased towards negative angles. In the quadrant analysis, the contribution of quadrants Q2 and Q4 is dominant on both the air side and the water side. The non-dimensional relative contributions and the concentration match fairly well near the interface. Sweeps in the air side (belonging to quadrant Q4) act directly on the interface and exert pressure fluctuations, which, in addition to the tangential stress and form drag, lead to the growth of the waves. The water drops detached from the crest and accelerated by the wind can play a major role in transferring momentum and in enhancing the turbulence level in the water side.On the air side, the Reynolds stress tensor's principal axes are not collinear with the strain rate tensor, and show an angle α σ≈=-20°to-25°. On the water side, the angle is α σ≈=-40°to-45°. The ratio between the maximum and the minimum principal stresses is σ a/σ b=3to4 on the air side, and σ a/σ b=1.5to3 on the water side. In this respect, the air-side flow behaves like a classical boundary layer on a solid wall, while the water-side flow resembles a wake. The frequency of bursting on the water side increases significantly along the flow, which can be attributed to micro-breaking effects - expected to be more frequent at larger fetches. © 2012 Elsevier B.V.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.