Software & Data Downloads

We present a novel approach to estimating camera rotation in crowded, real-world scenes from handheld monocular video. While camera rotation estimation (and more general motion estimation) is a well-studied problem, no previous methods exhibit both high accuracy and acceptable speed in this setting. Because the setting is not addressed well by other data sets, we provide a new dataset and benchmark, with high-accuracy, rigorously tested ground truth on 17 video sequences. Our method uses a novel generalization of the Hough transform on SO3 to efficiently find the camera rotation most compatible with the optical flow. Methods developed for wide baseline stereo (e.g., 5-point methods) do not do well with the small baseline implicit in . . .

Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis. While methods that use style-based GANs can generate strikingly photorealistic face images, it is often difficult to control the characteristics of the generated faces in a meaningful and disentangled way. Prior approaches aim to achieve such semantic control and disentanglement within the latent space of a previously trained GAN. In contrast, we propose a framework that a priori models physical attributes of the face such as 3D shape, albedo, pose, and lighting explicitly, thus providing disentanglement by design. Our method, MOST-GAN, integrates the expressive power and photorealism of style-based GANs with the physical . . .

Anomaly detection (AD) aims to identify defective images and localize their defects (if any). Ideally, AD models should be able to: detect defects over many image classes; not rely on hard-coded class names that can be uninformative or inconsistent across datasets; learn without anomaly supervision; and be robust to the long-tailed distributions of real-world applications. To address these challenges, we formulate the problem of long-tailed AD by introducing several datasets with different levels of class imbalance for performance evaluation.

PyTorch implementation for training and evaluating models proposed in our ICASSP 2024 paper, “NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization.” Both single and multi-subject training and inference codes are included for use with CIPIC and HUTUBS datasets, respectively.

This repository contains the code for the paper, Pixel-Grounded Prototypical Part Networks by Zachariah Carmichael, Suhas Lohit, Anoop Cherian, Michael Jones, and Walter J Scheirer. PixPNet (Pixel-Grounded Prototypical Part Network) is an improvement upon existing prototypical part neural networks (ProtoPartNNs): PixPNet truly localizes to object parts (unlike other approaches, including ProtoPNet), has quantitatively better interpretability, and is competitive on image classification benchmarks.

Prototypical part neural networks (ProtoPartNNs), namely ProtoPNet and its derivatives, are an intrinsically interpretable approach to machine learning. Their prototype learning scheme enables intuitive explanations of the form, this . . .

RANSAC-based algorithms are the standard techniques for robust estimation in computer vision. These algorithms are iterative and computationally expensive; they alternate between random sampling of data, computing hypotheses, and running inlier counting. Many authors tried different approaches to improve efficiency. One of the major improvements is having a guided sampling, letting the RANSAC cycle stop sooner. This paper presents a new guided sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously computed scores in the sampling. In this paper, we derive a dynamic Bayesian network that updates individual data points' inlier scores while . . .

Recent developments in wave-based sensor technologies, such as ground penetrating radar (GPR), provide new opportunities for imaging underground scenes. From the scattered electromagnetic wave measurements obtained by GPR, the goal is to estimate the permittivity distribution of the underground scenes. However, such problems are highly ill-posed, difficult to formulate, and computationally expensive. In this paper, we propose to use a novel physics-inspired machine learning-based method to learn the wave-matter interaction under the GPR setting. The learned forward model is combined with a learned signal prior to recover the unknown underground scenes via optimization. We test our approach on a dataset of 400 permittivity maps with three . . .

Recent years have seen embodied visual navigation advance in two distinct directions: (i) in equipping the AI agent to follow natural language instructions, and (ii) in making the navigable world multimodal, e.g., audio-visual navigation. However, the real world is not only multimodal, but also often complex, and thus in spite of these advances, agents still need to understand the uncertainty in their actions and seek instructions to navigate. To this end, we present AVLEN -- an interactive agent for Audio-Visual-Language Embodied Navigation. Similar to audio-visual navigation tasks, the goal of our embodied agent is to localize an audio event via navigating the 3D visual world; however, the agent may also seek help from a human (oracle), . . .

PyTorch implementation for training and interacting with models proposed in our ICASSP 2023 paper, “Hyperbolic Audio Source Separation.” We include the weights for a model pre-trained on the Librispeech Slakh Unmix (LSX) dataset, which hierarchically separates an audio mixture containing music and speech. Furthermore, code for training models using mask cross-entropy, spectrogram, and waveform losses is included. An interface for interacting with the learned hyperbolic embeddings created using PyQT6 is also provided in this codebase.

Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, ChatGPT, etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task (and the associated SMART-101 dataset) for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children of younger age (6--8). Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their . . .

One-class learning is the problem of fitting a model to data for which annotations are available only for a single class. Such models are useful for tasks such as anomaly detection, when the normal data is modeled by the 'one' class. In this software release, we are making public our implementation of our Generalized One-Class Discriminative Subspaces (GODS) algorithm (ICCV 2019, TPAMI 2023) for anomaly detection. The key idea of our method is to use a pair of orthonormal frames -- identifying the one-class data subspace -- to "sandwich" the labeled data via optimizing for two objectives jointly: i) minimize the distance between the origins of the two frames, and ii) to maximize the margin between the hyperplanes and the data. Our method . . .

PyTorch implementation of the Multi Resolution CrossNet (MRX) model proposed in our ICASSP 2022 paper, "The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks." We include the weights for a model pre-trained on the Divide and Remaster (DnR) dataset, which can separate the audio from a soundtrack (e.g., movie or commercial) into individual speech, music, and sound effects stems. A pytorch_lightning script for model training using the DnR dataset is also included.

This software package provides the PyTorch implementation of Partial Group Convolutional Neural Networks described in the NeurIPS 2022 paper "Learning Partial Equivariances from Data". Partial G-CNNs are able to learn layer-wise levels of partial and full equivariance to discrete, continuous groups and combinations thereof, directly from data. Partial G-CNNs retain full equivariance when beneficial, but adjust it whenever it becomes harmful. The software package also provides scripts to reproduce the results in the paper.

PyTorch reimplementation of code from "Nonparametric Score Estimators" (Yuhao Zhou, Jiaxin Shi, Jun Zhu. https://arxiv.org/abs/2005.10099). See original Tensorflow implementation at https://github.com/miskcoo/kscore (MIT license).

SOCKET allows transferring knowledge from neural networks trained on a source sensor modality (such as RGB) for one or more domains where large amount of annotated data may be available to an unannotated target dataset from a different sensor modality (such as infrared or depth). It makes use of task-irrelevant paired source-target images in order to promote feature alignment between the two modalities as well as distribution matching between the source batch norm features (mean and variance) and the target features.

This software package implements the CISOR reconstruction algorithm along with other benchmark algorithms that attempt to recover the distribution of refractive indices of an object in a multiple scattering regime. The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications. While the most popular formulations of the problem are based on linearizing the object-light relationship, there is an increased interest in considering nonlinear formulations that can account for multiple light scattering. Our proposed algorithm for nonlinear diffractive imaging, called Convergent Inverse Scattering using Optimization and Regularization (CISOR), is based on our new variant of fast . . .

This package implements InSeGAN, an unsupervised 3D generative adversarial network (GAN) for segmenting (nearly) identical instances of rigid objects in depth images. For this task, we design a novel GAN architecture to synthesize a multiple-instance depth image with independent control over each instance. InSeGAN takes in a set of code vectors (e.g., random noise vectors), each encoding the 3D pose of an object that is represented by a learned implicit object template. The generator has two distinct modules. The first module, the instance feature generator, uses each encoded pose to transform the implicit template into a feature map representation of each object instance. The second module, the depth image renderer, aggregates all of the . . .

Many sounds that humans encounter are hierarchical in nature; a piano note is one of many played during a performance, which is one of many instruments in a band, which might be playing in a bar with other noises occurring. Inspired by this, we re-frame the musical source separation problem as hierarchical, combining similar instruments together at certain levels and separating them at other levels. This allows us to deconstruct the same mixture in multiple ways, depending on the appropriate level of the hierarchy for a given application. In this software package, we present pytorch implementations of various methods for hierarchical musical instrument separation, with some methods focusing on separating specific instruments (like guitars) . . .

State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to characterize the sources better, especially when the same object class may produce varied sounds from distinct interactions. To address this challenging problem, we propose Audio Visual Scene Graph Segmenter (AVSGS), a novel deep learning model that embeds the visual structure of the scene as a graph and segments this graph into subgraphs, each subgraph being associated with a unique sound obtained via co-segmenting the audio spectrogram. At its core, . . .

PyRoboCOP is a lightweight Python-based package for control and optimization of robotic systems described by nonlinear Differential Algebraic Equations (DAEs). In particular, the package can handle systems with contacts that are described by complementarity constraints and provides a general framework for specifying obstacle avoidance constraints. The package performs direct transcription of the DAEs into a set of nonlinear equations by performing orthogonal collocation on finite elements. The resulting optimization problem belongs to the class of Mathematical Programs with Complementarity Constraints (MPCCs). MPCCs fail to satisfy commonly assumed constraint qualifications and require special handling of the complementarity constraints in . . .

This package implements a Model-based Reinforcement Learning algorithm called Monte Carlo Probabilistic Inference for Learning and COntrol (MC-PILCO), for modeling and control of dynamical system. The algorithm relies on Gaussian Processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient during optimization. The Monte Carlo approach is shown to be effective for policy optimization thanks to a proper cost function shaping and use of dropout. The possibility of using a Monte Carlo approach allows a more flexible framework for Gaussian Process Regression that leads to more structured and more data efficient kernels. The algorithm is also extended to work for Partially Measurable Systems and . . .

In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can efficiently learn to understand and generalize to different environments using high-dimensional inputs (a 2D map), while following feasible paths that avoid obstacles in obstacle-cluttered environment. We test our proposed method in the recently proposed \textit{Safety Gym} suite that allows testing of safety-constraints during training of learning agents. The provided python code base allows to reproduce the results from the IROS 2020 paper that was published last year.

Learning associations across modalities is critical for robust multimodal reasoning, especially when a modality may be missing during inference. In this paper, we study this problem in the context of audio-conditioned visual synthesis -- a task that is important, for example, in occlusion reasoning. Specifically, our goal is to generate video frames and their motion dynamics conditioned on audio and a few past frames. To tackle this problem, we present Sound2Sight, a deep variational framework, that is trained to learn a per frame stochastic prior conditioned on a joint embedding of audio and past frames. This embedding is learned via a multi-head attention-based audio-visual transformer encoder. The learned prior is then sampled to . . .

Quantum Annealing (QA) can be used to quickly obtain near-optimal solutions for Quadratic Unconstrained Binary Optimization (QUBO) problems. In QA hardware, each decision variable of a QUBO should be mapped to one or more adjacent qubits in such a way that pairs of variables defining a quadratic term in the objective function are mapped to some pair of adjacent qubits. However, qubits have limited connectivity in existing QA hardware. This software Python codes implementing integer linear programs to search for an embedding of the problem graph into certain classes of minors of the QA hardware, which we call template embeddings. In particular, we consider the template embedding that are minors of the Chimera graph used in D-Wave . . .

In this software release, we provide a PyTorch implementation of the adversarially-contrastive optimal transport (ACOT) algorithm. Through ACOT, we study the problem of learning compact representations for sequential data that captures its implicit spatio-temporal cues. To separate such informative cues from the data, we propose a novel contrastive learning objective via optimal transport. Specifically, our formulation seeks a low-dimensional subspace representation of the data that jointly (i) maximizes the distance of the data (embedded in this subspace) from an adversarial data distribution under a Wasserstein distance, (ii) captures the temporal order, and (iii) minimizes the data distortion. To generate the adversarial distribution, . . .

In this package, we provide python code for a circular maze environment (CME) which Is a challenging environment for learning manipulation and control. The goal in this system is to tip and tilt the CME so as to drive one (or more) marble(s) from the outermost to the innermost ring. While this system is very intuitive and easy for humans to solve, it can be very difficult and inefficient for standard reinforcement learning algorithms to learn meaningful policies. Consequently, we provide codes to this environment so that it can be used as a benchmark for different algorithms that can learn meaningful policies in this environment. We also provide codes for iLQR which can be used to control the motion of marbles in the proposed environment.

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained using our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the . . .

Learning accurate models of the physical world is required for a lot of robotic manipulation tasks. However, during manipulation, robots are expected to interact with unknown workpieces so that building predictive models which can generalize over a number of these objects is highly desirable. We provide codes for context-aware zero shot learning (CAZSL) models, an approach utilizing a Siamese network architecture, embedding space masking and regularization based on context variables which allows us to learn a model that can generalize to different parameters or features of the interacting objects. The proposed learning algorithm on the recently released Omnipush data set that allows testing of meta-learning capabilities using . . .

This Python code implements an online feature extractor network (OFENet) that uses neural nets to produce good representations to be used as inputs to deep RL algorithms. Even though the high dimensionality of input is usually supposed to make learning of RL agents more difficult, by using this network, we show that the RL agents in fact learn more efficiently with the high-dimensional representation than with the lower-dimensional state observations. We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency. The code also contains several test problems. Through numerical experiments on these . . .

The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we propose an efficient deep model, called MotionNet, to jointly perform perception and motion prediction from 3D point clouds. MotionNet takes a sequence of LiDAR sweeps as input and outputs a bird's eye view (BEV) map, which encodes the object category and motion information in each grid cell. The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel . . .

This software is the pytorch implementation of FoldingNet++, which is a novel end-to-end graph-based deep autoencoder to achieve compact representations of unorganized 3D point clouds in an unsupervised manner.

The encoder of the proposed networks adopts similar architectures as in PointNet, which is a well-acknowledged method for supervised learning of 3D point clouds, such as recognition and segmentation. The decoder of the proposed networks involves three novel modules: folding module, graph-topology-inference module, and graph-filtering module. The folding module folds a canonical 2D lattice to the underlying surface of a 3D point cloud, achieving coarse reconstruction; the graph-topology-inference module learns a graph . . .

We propose a trust region method for policy optimization that employs Quasi-Newton approximation for the Hessian, called Quasi-Newton Trust Region Policy Optimization (QNTRPO). Gradient descent has become the de facto algorithm for reinforcement learning tasks with continuous controls. The algorithms has achieved state-of-the-art performance on wide variety of tasks and resulted in several improvements in performance of reinforcement learning algorithms across a wide range of systems. However, the algorithm suffers from a number of drawbacks including: lack of stepsize selection criterion, slow convergence, and dependence on problem scaling. We investigate the use of a dogleg method with a Quasi-Newton approximation for the Hessian to . . .

Recent studies have demonstrated that as classifiers, deep neural networks (e.g., CNNs) are quite vulnerable to adversarial attacks that only add quasi-imperceptible perturbations to the input data but completely change the predictions of the classifiers. To defend classifiers against such adversarial attacks, here we focus on the white-box adversarial defense where the attackers are granted full access to not only the classifiers but also defenders to produce as strong attack as possible. We argue that a successful white-box defender should prevent the attacker from not only direct gradient calculation but also a gradient approximation. Therefore we propose viewing the defense from the perspective of a functional, a high-order function . . .

Computing Nash equilibrium (NE) of multiplayer games has witnessed renewed interest due to recent advances in generative adversarial networks (GAN). However, computing equilibrium efficiently is challenging. To this end, we introduce the Gradient-based Nikaido-Isoda (GNI) function which serves as a merit function, vanishing only at the first-order stationary points of each player’s optimization problem. Gradient descent is shown to converge sublinearly to a first-order stationary point of the GNI function. For the particular case of bilinear min-max games and multi-player quadratic games, the GNI function is convex. Hence, the application of gradient descent in this case yields linear convergence to an NE (when one exists).
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Human action recognition from video sequences is one of the fundamental problems in computer vision. In this research, we investigate and propose representation learning approaches towards solving this problem, which we call discriminative subspace pooling. Specifically, we combine recent deep learning approaches with techniques for generating adversarial perturbations into learning novel representations that can summarize long video sequences into compact descriptors – these descriptors capture essential properties of the input videos that are sufficient to achieve good recognition rates. We make two contributions. First, we propose a subspace-based discriminative classifier, similar to a non-linear SVM, but having piecewise-linear . . .

The Street Scene dataset consists of 46 training video sequences and 35 testing video sequences taken from a static USB camera looking down on a scene of a two-lane street with bike lanes and pedestrian sidewalks. See Figure 1 for a typical frame from the dataset. Videos were collected from the camera at various times during two consecutive summers. All of the videos were taken during the daytime. The dataset is challenging because of the variety of activity taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition, the videos contain changing shadows, and moving background such as a flag and trees blowing in the wind.
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Successful state-of-the-art machine learning techniques rely on the existence of large well sampled and labeled datasets. Today it is easy to obtain a finely sampled dataset because of the decreasing cost of connected low-energy devices. However, it is often difficult to obtain a large number of labels. The reason for this is two-fold. First, labels are often provided by people whose attention span is limited. Second, even if a person was able to label perpetually, this person would need to be shown data in a large variety of conditions. One approach to addressing these problems is to combine labeled data collected in different sessions through transfer learning. Still even this approach suffers from dataset limitations.

This . . .

Massive multiple-input multiple-output (MIMO) systems can significantly increase the spectral efficiency, mitigate propagation loss by exploiting large array gain, and reduce inter-user interference with high-resolution spatial beamforming. To reduce complexity and power consumption, several transceiver architectures have been proposed for mmWave massive MIMO systems: 1) an analog architecture, 2) a hybrid analog/digital architecture, and 3) a fully digital architecture with low-resolution ADCs.

To this end, we derive the Cramer-Rao bound (CRB) on estimating angular-domain channel parameters including angles-of-departure (AoDs), angles-of-arrival (AoAs), and associated channel path gains. Our analysis provides a simple tool . . .

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 . . .

Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud, achieving low reconstruction errors even for objects with delicate structures. The proposed decoder only uses about 7% parameters of a decoder with fully-connected neural networks, yet leads to a more . . .

We propose a randomized resampling strategy to reduce the cost of storing, processing and visualizing a large-scale point cloud, that selects a representative subset of points while preserving application-dependent features. The strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling; by minimizing the error, we obtain a general form of optimal resampling distribution. The proposed resampling distribution is guaranteed to be shift-, rotation- and scale-invariant in the 3D space.

It is challenging to measure the geometry distortion of point cloud introduced by point cloud compression. Conventionally, the errors between point clouds are measured in terms of point-to-point or point-to-surface distances, that either ignores the surface structures or heavily tends to rely on specific surface reconstructions. To overcome these drawbacks, we propose using point-to-plane distances as a measure of geometric distortions on point cloud compression. The intrinsic resolution of the point clouds is proposed as a normalizer to convert the mean square errors to PSNR numbers. In addition, the perceived local planes are investigated at different scales of the point cloud. Finally, the proposed metric is independent of the size of . . .

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.

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 . . .

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 . . .

As part of this research, we collected a new dataset for training and testing action detection algorithms. Our MERL Shopping Dataset consists of 106 videos, each of which is a sequence about 2 minutes long. The videos are from a fixed overhead camera looking down at people shopping in a grocery store setting. Each video contains several instances of the following 5 actions: "Reach To Shelf" (reach hand into shelf), "Retract From Shelf " (retract hand from shelf), "Hand In Shelf" (extended period with hand in the shelf), "Inspect Product" (inspect product while holding it in hand), and "Inspect Shelf" (look at shelf while not touching or reaching for the shelf).

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 . . .

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 . . .

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 . . .

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 . . .

The MERL BRDF database contains reflectance functions of 100 different materials. Each reflectance function is stored as a densely measured Bidirectional Reflectance Distribution Function (BRDF).

Sample code to read the data is included with the database. Note that parameterization of theta-half has changed.