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MERL software freely available for non-commercial use.

MERL is making some research available for non-commercial licensing. Simply click the 'Download Now' button below to gain access to the software.

  • PEAC — Plane Extraction using Agglomerative Clustering

    Real-time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into non-overlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640x480 point clouds, which to the best of our knowledge is much faster than state-of-the-art algorithms.

  • JGU — Joint Geodesic Upsampling

    We develop an algorithm utilizing geodesic distances to upsample a low resolution depth image using a registered high resolution color image. Specifically, it computes depth for each pixel in the high resolution image using geodesic paths to the pixels whose depths are known from the low resolution one. Though this is closely related to the all-pairshortest-path problem which has O(n2 log n) complexity, we develop a novel approximation algorithm whose complexity grows linearly with the image size and achieve real-time performance. We compare our algorithm with the state of the art on the benchmark dataset and show that our approach provides more accurate depth upsampling with fewer artifacts. In addition, we show that the proposed algorithm is well suited for upsampling depth images using binary edge maps, an important sensor fusion application.

  • ROSETA — Robust Online Subspace Estimation and Tracking Algorithm

    This script implements a revised version of the robust online subspace estimation and tracking algorithm (ROSETA) that is capable of identifying and tracking a time-varying low dimensional subspace from incomplete measurements and in the presence of sparse outliers. The algorithm minimizes a robust l1 norm cost function between the observed measurements and their projection onto the estimated subspace. The projection coefficients and sparse outliers are computed using a LASSO solver and the subspace estimate is updated using a proximal point iteration with adaptive parameter selection.

  • PQP — Parallel Quadratic Programming

    An iterative multiplicative algorithm is proposed for the fast solution of quadratic programming (QP) problems that arise in the real-time implementation of Model Predictive Control (MPC). The proposed algorithm—Parallel Quadratic Programming (PQP)—is amenable to fine-grained parallelization. Conditions on the convergence of the PQP algorithm are given and proved. Due to its extreme simplicity, even serial implementations offer considerable speed advantages. To demonstrate, PQP is applied to several simulation examples, including a stand-alone QP problem and two MPC examples. When implemented in MATLAB using single-thread computations, numerical simulations of PQP demonstrate a 5 - 10x speed-up compared to the MATLAB active-set based QP solver quadprog. A parallel implementation would offer a further speed-up, linear in the number of parallel processors.

  • EBAD — Exemplar-Based Anomaly Detection

    Anomaly detection in real-valued time series has important applications in many diverse areas. We have developed a general algorithm for detecting anomalies in real-valued time series that is computationally very efficient. Our algorithm is exemplar-based which means a set of exemplars are first learned from a normal time series (i.e. not containing any anomalies) which effectively summarizes all normal windows in the training time series. Anomalous windows of a testing time series can then be efficiently detected using the exemplar-based model.

    The provided code implements our hierarchical exemplar learning algorithm, our exemplar-based anomaly detection algorithm, and a baseline brute-force Euclidean distance anomaly detection algorithm. Two simple time series are also provided to test the code.

  • NDS — Non-negative dynamical system model

    Non-negative data arise in a variety of important signal processing domains, such as power spectra of signals, pixels in images, and count data. We introduce a novel non-negative dynamical system model for sequences of such data. The model we propose is called non-negative dynamical system (NDS), and bridges two active fields, dynamical systems and nonnegative matrix factorization (NMF). Its formulation follows that of linear dynamical systems, but the observation and the latent variables are assumed non-negative, the linear transforms are assumed to involve non-negative coefficients, and the additive random innovations both for the observation and the latent variables are replaced by multiplicative random innovations. The software includes code for training and testing, as well as a simple framework for applying this model to the task of speech enhancement.

  • CASENet — Deep Category-Aware Semantic Edge Detection

    Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard datasets such as SBD and Cityscapes.