3D Pose Estimation and 3D Model Retrieval for Objects in the Wild

We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contribution is twofold. We first present a 3D pose estimation approach for object categories which significantly outperforms the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior to retrieve 3D models which accurately represent the geometry of objects in RGB images. For this purpose, we render depth images from 3D models under our predicted pose and match learned image descriptors of RGB images against those of rendered depth images using a CNN-based multi-view metric learning approach. In this way, we are the first to report quantitative results for 3D model retrieval on Pascal3D+, where our method chooses the same models as human annotators for 50% of the validation images on average. In addition, we show that our method, which was trained purely on Pascal3D+, retrieves rich and accurate 3D models from ShapeNet given RGB images of objects in the wild.Comment: Accepted to Conference on Computer Vision and Pattern Recognition (CVPR) 201

Global first-passage times of fractal lattices

The global first passage time density of a network is the probability that a random walker released at a random site arrives at an absorbing trap at time T. We find simple expressions for the mean global first passage time for five fractals: the d-dimensional Sierpinski gasket, T fractal, hierarchical percolation model, Mandelbrot-Given curve, and a deterministic tree. We also find an exact expression for the second moment and show that the variance of the first passage time, Var(T), scales with the number of nodes within the fractal N such that Var(T)similar to N(4/d), where d is the spectral dimension

SPECT- and PET-Based Approaches for Noninvasive Diagnosis of Acute Renal Allograft Rejection

Molecular imaging techniques such as single photon emission computed tomography (SPECT) or positron emission tomography are promising tools for noninvasive diagnosis of acute allograft rejection (AR). Given the importance of renal transplantation and the limitation of available donors, detailed analysis of factors that affect transplant survival is important. Episodes of acute allograft rejection are a negative prognostic factor for long-term graft survival. Invasive core needle biopsies are still the “goldstandard” in rejection diagnostics. Nevertheless, they are cumbersome to the patient and carry the risk of significant graft injury. Notably, they cannot be performed on patients taking anticoagulant drugs. Therefore, a noninvasive tool assessing the whole organ for specific and fast detection of acute allograft rejection is desirable. We herein review SPECT- and PET-based approaches for noninvasive molecular imaging-based diagnostics of acute transplant rejection

Existence of a Meromorphic Extension of Spectral Zeta Functions on Fractals

We investigate the existence of the meromorphic extension of the spectral zeta function of the Laplacian on self-similar fractals using the classical results of Kigami and Lapidus (based on the renewal theory) and new results of Hambly and Kajino based on the heat kernel estimates and other probabilistic techniques. We also formulate conjectures which hold true in the examples that have been analyzed in the existing literature

GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild

We present a joint 3D pose and focal length estimation approach for object categories in the wild. In contrast to previous methods that predict 3D poses independently of the focal length or assume a constant focal length, we explicitly estimate and integrate the focal length into the 3D pose estimation. For this purpose, we combine deep learning techniques and geometric algorithms in a two-stage approach: First, we estimate an initial focal length and establish 2D-3D correspondences from a single RGB image using a deep network. Second, we recover 3D poses and refine the focal length by minimizing the reprojection error of the predicted correspondences. In this way, we exploit the geometric prior given by the focal length for 3D pose estimation. This results in two advantages: First, we achieve significantly improved 3D translation and 3D pose accuracy compared to existing methods. Second, our approach finds a geometric consensus between the individual projection parameters, which is required for precise 2D-3D alignment. We evaluate our proposed approach on three challenging real-world datasets (Pix3D, Comp, and Stanford) with different object categories and significantly outperform the state-of-the-art by up to 20% absolute in multiple different metrics.Comment: Accepted to International Conference on Computer Vision (ICCV) 201

Location Field Descriptors: Single Image 3D Model Retrieval in the Wild

We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild. In contrast to previous methods that directly map 3D models and RGB images to an embedding space, we establish a common low-level representation in the form of location fields from which we compute pose invariant 3D shape descriptors. Location fields encode correspondences between 2D pixels and 3D surface coordinates and, thus, explicitly capture 3D shape and 3D pose information without appearance variations which are irrelevant for the task. This early fusion of 3D models and RGB images results in three main advantages: First, the bottleneck location field prediction acts as a regularizer during training. Second, major parts of the system benefit from training on a virtually infinite amount of synthetic data. Finally, the predicted location fields are visually interpretable and unblackbox the system. We evaluate our proposed approach on three challenging real-world datasets (Pix3D, Comp, and Stanford) with different object categories and significantly outperform the state-of-the-art by up to 20% absolute in multiple 3D retrieval metrics.Comment: Accepted to International Conference on 3D Vision (3DV) 2019 (Oral

Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning

Hydroxyfasudil-Mediated Inhibition of ROCK1 and ROCK2 Improves Kidney Function in Rat Renal Acute Ischemia-Reperfusion Injury

Renal ischemia-reperfusion (IR) injury (IRI) is a common and important trigger of acute renal injury (AKI). It is inevitably linked to transplantation. Involving both, the innate and the adaptive immune response, IRI causes subsequent sterile inflammation. Attraction to and transmigration of immune cells into the interstitium is associated with increased vascular permeability and loss of endothelial and tubular epithelial cell integrity. Considering the important role of cytoskeletal reorganization, mainly regulated by RhoGTPases, in the development of IRI we hypothesized that a preventive, selective inhibition of the Rho effector Rho-associated coiled coil containing protein kinase (ROCK) by hydroxyfasudil may improve renal IRI outcome. Using an IRI-based animal model of AKI in male Sprague Dawley rats, animals treated with hydroxyfasudil showed reduced proteinuria and polyuria as well as increased urine osmolarity when compared with sham-treated animals. In addition, renal perfusion (as assessed by 18F-fluoride Positron Emission Tomography (PET)), creatinine- and urea-clearances improved significantly. Moreover, endothelial leakage and renal inflammation was significantly reduced as determined by histology, 18F-fluordesoxyglucose-microautoradiography, Evans Blue, and real-time PCR analysis. We conclude from our study that ROCK-inhibition by hydroxyfasudil significantly improves kidney function in a rat model of acute renal IRI and is therefore a potential new therapeutic option in humans