Learning quadrangulated patches for 3D shape parameterization and completion

We propose a novel 3D shape parameterization by surface patches, that are oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape. Unlike previous methods, we are able to encode surface patches of variable size as determined by the user. We propose novel methods for dictionary learning and patch reconstruction based on the query of a noisy input patch with holes. We evaluate the patch dictionary towards various applications in 3D shape inpainting, denoising and compression. Our method is able to predict missing vertices and inpaint moderately sized holes. We demonstrate a complete pipeline for reconstructing the 3D mesh from the patch encoding. We validate our shape parameterization and reconstruction methods on both synthetic shapes and real world scans. We show that our patch dictionary performs successful shape completion of complicated surface textures.Comment: To be presented at International Conference on 3D Vision 2017, 201

A study on diagnostic efficacy of pulmonary imaging tool in patients with rheumatoid arthritis

Background: Pulmonary complication in Rheumatoid arthritis is major health concern in the field of rheumatology. So this study is to find out the sensitive imaging tool for detecting different types of pulmonary changes seen in RA.Methods: This is a comparative, analytical, cross sectional, Institution- based, single centre study. We included all adult willing patients of Rheumatoid arthritis (age >18) and selected them based on 2010 ACR/EULAR criteria. Severity was assessed by number of joints involve in both upper and lower limb, along with ESR, CRP, Anti CCP level. Chest x ray, PFT and HRCT thorax were done in all RA patients.Results: Out of 50 RA patients, pulmonary involvement was observed in 25 patients identified by Spirometry, CXR, HRCT. Pulmonary involvement is more common in age group <40 years. Most common form of pulmonary involvement is ILD followed by obstructive lung disease like chronic bronchitis, bronchiectasis etc. HRCT is the most common tool for detection of Pulmonary involvement in rheumatoid arthritis. HRCT abnormality, most are Restrictive on spirometry (FEV1/FVC) <80% of predicted value. And this relation is statistically significant as P value is 0.001 (<0.05) by Chi-Square test. Patients having more the disease duration, more the pulmonary involvement. This association is statically significant as p value is 0.001 (<0.05).Conclusions: High resolution CT thorax is more sensitive modality for detection of pulmonary pathology in rheumatoid arthritis. Because of its high cost, availability of this imaging technique is beyond the lower socio-economic group where chest x ray may be useful

Scene-aware Egocentric 3D Human Pose Estimation

Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Existing methods still struggle in challenging poses where the human body is highly occluded or is closely interacting with the scene. To address this issue, we propose a scene-aware egocentric pose estimation method that guides the prediction of the egocentric pose with scene constraints. To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network. Next, we propose a scene-aware pose estimation network that projects the 2D image features and estimated depth map of the scene into a voxel space and regresses the 3D pose with a V2V network. The voxel-based feature representation provides the direct geometric connection between 2D image features and scene geometry, and further facilitates the V2V network to constrain the predicted pose based on the estimated scene geometry. To enable the training of the aforementioned networks, we also generated a synthetic dataset, called EgoGTA, and an in-the-wild dataset based on EgoPW, called EgoPW-Scene. The experimental results of our new evaluation sequences show that the predicted 3D egocentric poses are accurate and physically plausible in terms of human-scene interaction, demonstrating that our method outperforms the state-of-the-art methods both quantitatively and qualitatively