Performance evaluation and enhancement of Dendro

DENDRO is a collection of tools for solving Finite Element problems in parallel. This package is written in C++ using the standard template library (STL) and uses the Message Passing (MPI). Dendro uses an octree data-structure to solve image-registration problems using finite element techniques. For analyzing the behavior of the package in terms of speed-up and scalability, it is important to know which part of the package is consuming most of the execution-time. The single node performance and the overall performance of the package is dependent on the code-organization and class-hierarchy. We used the PETSC profiler to collect the performance statistics and instrument the code to know which part of the code takes most of the time. Along with the function-specific execution timings, PETSC profiler also provides the information regarding how many floating point operations is being performed in total and on average (FLOP/second). PETSC also provides information related to memory usage and number of MPI messages and reductions being performed to execute that particular function. We have analyzed these performance-statistics to provide some guidelines to how we can make Dendro more efficient by optimizing certain functions. We obtained around 12X speedup over the performance of (default) Dendro by using compiler-provided optimizations and achieved more than 65% speedup over compiler optimized performance (20X over the naive Dendro performance) by manually tuning some-block of code along with the compiler-optimizations

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with a more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a set of learnable 3D points that jointly respond to a set of neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly achieve better performances on major datasets. Our code is available at http://www.merl.com/research/license#KCNetComment: Accepted in CVPR'18. *indicates equal contributio

Registration using Graphics Processor Unit

Data point set registration is an important operation in coordinate metrology. Registration is the operation by which sampled point clouds are aligned with a CAD model by a 4X4 homogeneous transformation (e.g., rotation and translation). This alignment permits validation of the produced artifact\u27s geometry. State-of-the-art metrology systems are now capable of generating thousands, if not millions, of data points during an inspection operation, resulting in increased computational power to fully utilize these larger data sets. The registration process is an iterative nonlinear optimization operation having an execution time directly related to the number of points processed and CAD model complexity. The objective function to be minimized by this optimization is the sum of the square distances between each point in the point cloud and the closest surface in the CAD model. A brute force approach to registration, which is often used, is to compute the minimum distance between each point and each surface in the CAD model. As point cloud sizes and CAD model complexity increase, this approach becomes intractable and inefficient. Highly efficient numerical and analytical gradient based algorithms exist and their goal is to convergence to an optimal solution in minimum time. This thesis presents a new approach to efficiently perform the registration process by employing readily available computer hardware, the graphical processor unit (GPU). The data point set registration time for the GPU shows a significant improvement (around 15-20 times) over typical CPU performance. Efficient GPU programming decreases the complexity of the steps and improves the rate of convergence of the existing algorithms. The experimental setup reveals the exponential increasing nature of the CPU and the linear performance of the GPU in various aspects of an algorithm. The importance of CPU in the GPU programming is highlighted. The future implementations disclose the possible extensions of a GPU for higher order and complex coordinate metrology algorithms

MRI/TRUS data fusion for brachytherapy

BACKGROUND: Prostate brachytherapy consists in placing radioactive seeds for tumour destruction under transrectal ultrasound imaging (TRUS) control. It requires prostate delineation from the images for dose planning. Because ultrasound imaging is patient- and operator-dependent, we have proposed to fuse MRI data to TRUS data to make image processing more reliable. The technical accuracy of this approach has already been evaluated. METHODS: We present work in progress concerning the evaluation of the approach from the dosimetry viewpoint. The objective is to determine what impact this system may have on the treatment of the patient. Dose planning is performed from initial TRUS prostate contours and evaluated on contours modified by data fusion. RESULTS: For the eight patients included, we demonstrate that TRUS prostate volume is most often underestimated and that dose is overestimated in a correlated way. However, dose constraints are still verified for those eight patients. CONCLUSIONS: This confirms our initial hypothesis

As-Built 3D Heritage City Modelling to Support Numerical Structural Analysis: Application to the Assessment of an Archaeological Remain

Terrestrial laser scanning is a widely used technology to digitise archaeological, architectural and cultural heritage. This allows for modelling the assets’ real condition in comparison with traditional data acquisition methods. This paper, based on the case study of the basilica in the Baelo Claudia archaeological ensemble (Tarifa, Spain), justifies the need of accurate heritage modelling against excessively simplified approaches in order to support structural safety analysis. To do this, after validating the 3Dmeshing process frompoint cloud data, the semi-automatic digital reconstitution of the basilica columns is performed. Next, a geometric analysis is conducted to calculate the structural alterations of the columns. In order to determine the structural performance, focusing both on the accuracy and suitability of the geometric models, static and modal analyses are carried out by means of the finite element method (FEM) on three different models for the most unfavourable column in terms of structural damage: (1) as-built (2) simplified and (3) ideal model without deformations. Finally, the outcomes show that the as-built modelling enhances the conservation status analysis of the 3D heritage city (in terms of realistic compliance factor values), although further automation still needs to be implemented in the modelling process

Shape Generation using Spatially Partitioned Point Clouds

We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points. This orders them consistently across all shapes, resulting in reasonably good correspondences across all shapes. We then use PCA analysis to derive a linear shape basis across the spatially partitioned points, and optimize the point ordering by iteratively minimizing the PCA reconstruction error. Even with the spatial sorting, the point clouds are inherently noisy and the resulting distribution over the shape coefficients can be highly multi-modal. We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework. Compared to 3D shape generative models trained on voxel-representations, our point-based method is considerably more light-weight and scalable, with little loss of quality. It also outperforms simpler linear factor models such as Probabilistic PCA, both qualitatively and quantitatively, on a number of categories from the ShapeNet dataset. Furthermore, our method can easily incorporate other point attributes such as normal and color information, an additional advantage over voxel-based representations.Comment: To appear at BMVC 201