DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport

Accurate, robust, and real-time LiDAR-based odometry (LO) is imperative for many applications like robot navigation, globally consistent 3D scene map reconstruction, or safe motion-planning. Though LiDAR sensor is known for its precise range measurement, the non-uniform and uncertain point sampling density induce structural inconsistencies. Hence, existing supervised and unsupervised point set registration methods fail to establish one-to-one matching correspondences between LiDAR frames. We introduce a novel deep learning-based real-time (approx. 35-40ms per frame) LO method that jointly learns accurate frame-to-frame correspondences and model's predictive uncertainty (PU) as evidence to safe-guard LO predictions. In this work, we propose (i) partial optimal transportation of LiDAR feature descriptor for robust LO estimation, (ii) joint learning of predictive uncertainty while learning odometry over driving sequences, and (iii) demonstrate how PU can serve as evidence for necessary pose-graph optimization when LO network is either under or over confident. We evaluate our method on KITTI dataset and show competitive performance, even superior generalization ability over recent state-of-the-art approaches. Source codes are available.Comment: Accepted in ICCV 2023 Worksho

Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations

This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of motion. Additional alignment cues (point-based or geometric features, and other boundary conditions) can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid prohibitive quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear computational complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous point sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing non-deep-learning-based and general-purpose techniques (which do not assume the availability of training data and a scene prior) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different types of data disturbances.Comment: 18 pages, 18 figures and two table

HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map

3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks, which leads to artefacts in the estimations due to perspective distortions in the images. In contrast, we propose a novel architecture with 3D convolutions trained in a weakly-supervised manner. The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations. The first one is the 3D voxelized grid of the shape which is accurate but does not preserve the mesh topology and the number of mesh vertices. The second representation is the 3D hand surface which is less accurate but does not suffer from the limitations of the first representation. We combine the advantages of these two representations by registering the hand surface to the voxelized hand shape. In the extensive experiments, the proposed approach improves over the state of the art by 47.8% on the SynHand5M dataset. Moreover, our augmentation policy for voxelized depth maps further enhances the accuracy of 3D hand pose estimation on real data. Our method produces visually more reasonable and realistic hand shapes on NYU and BigHand2.2M datasets compared to the existing approaches.Comment: 10 pages, 8 figures, 5 tables, CVP

TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network

Reconstructing 3D human body shapes from 3D partial textured scans remains a fundamental task for many computer vision and graphics applications – e.g., body animation, and virtual dressing. We propose a new neural network architecture for 3D body shape and highresolution texture completion – TSCom-Net – that can reconstruct the full geometry from mid-level to high-level partial input scans. We decompose the overall reconstruction task into two stages – first, a joint implicit learning network (SCom-Net and TCom-Net) that takes a voxelized scan and its occupancy grid as input to reconstruct the full body shape and predict vertex textures. Second, a high-resolution texture completion network, that utilizes the predicted coarse vertex textures to inpaint the missing parts of the partial ‘texture atlas’. A Thorough experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves competitive results with respect to the state-of-the-art while generalizing to different types and levels of partial shapes. The proposed method has also ranked second in the track1 of SHApe Recovery from Partial textured 3D scans (SHARP [37 , 2]) 2022 1 challenge1

SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines.

peer reviewedRecent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processes and relating them to real objects. In this context, 3D reverse engineering of CAD models from 3D scans is considered to be one of the most sought-after goals for the CAD industry. However, recent efforts assume multiple simplifications limiting the applications in real-world settings. The SHARP Challenge 2023 aims at pushing the research a step closer to the real-world scenario of CAD reverse engineering from 3D scans through dedicated datasets and tracks. In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions. All proposed datasets along with useful routines and the evaluation metrics are publicly available

The state of ambient air quality in Pakistan—a review

Background and purpose: Pakistan, during the last decade, has seen an extensive escalation in population growth, urbanization, and industrialization, together with a great increase in motorization and energy use. As a result, a substantial rise has taken place in the types and number of emission sources of various air pollutants. However, due to the lack of air quality management capabilities, the country is suffering from deterioration of air quality. Evidence from various governmental organizations and international bodies has indicated that air pollution is a significant risk to the environment, quality of life, and health of the population. The Government has taken positive steps toward air quality management in the form of the Pakistan Clean Air Program and has recently established a small number of continuous monitoring stations. However, ambient air quality standards have not yet been established. This paper reviews the data being available on the criteria air pollutants: particulate matter (PM), sulfur dioxide, ozone, carbon monoxide, nitrogen dioxide, and lead. Methods: Air pollution studies in Pakistan published in both scientific journals and by the Government have been reviewed and the reported concentrations of PM, SO2, O3, CO, NO2, and Pb collated. A comparison of the levels of these air pollutants with the World Health Organization air quality guidelines was carried out. Results: Particulate matter was the most serious air pollutant in the country. NO2 has emerged as the second high-risk pollutant. The reported levels of PM, SO2, CO, NO2, and Pb were many times higher than the World Health Organization air quality guidelines. Only O3 concentrations were below the guidelines. Conclusions: The current state of air quality calls for immediate action to tackle the poor air quality. The establishment of ambient air quality standards, an extension of the continuous monitoring sites, and the development of emission control strategies are essential. © Springer-Verlag 2009

The management of myocardial injury related to SARS-CoV-2 pneumonia

The global evolution of the SARS-CoV-2 virus is known to all. The diagnosis of SARS-CoV-2 pneumonia is expected to worsen, and mortality will be higher when combined with myocardial injury (MI). The combination of novel coronavirus infections in patients with MI can cause confusion in diagnosis and assessment, with each condition exacerbating the other, and increasing the complexity and difficulty of treatment. It would be a formidable challenge for clinical practice to deal with this situation. Therefore, this review aims to gather literature on the progress in managing MI related to SARS-CoV-2 pneumonia. This article reviews the definition, pathogenesis, clinical evaluation, management, and treatment plan for MI related to SARS-CoV-2 pneumonia based on the most recent literature, diagnosis, and treatment trial reports. Many studies have shown that early diagnosis and implementation of targeted treatment measures according to the different stages of disease can reduce the mortality rate among patients with MI related to SARS-CoV-2 pneumonia. The reviewed studies show that multiple strategies have been adopted for the management of MI related to COVID-19. Clinicians should closely monitor SARS-CoV-2 pneumonia patients with MI, as their condition can rapidly deteriorate and progress to heart failure, acute myocardial infarction, and/or cardiogenic shock. In addition, appropriate measures need to be implemented in the diagnosis and treatment to provide reasonable care to the patient

Burden of waterpipe smoking and chewing tobacco use among women of reproductive age group using data from the 2012-13 Pakistan Demographic and Health Survey

Background: Despite the general decline in cigarette smoking, use of alternative forms of tobacco has increased particularly in developing countries. Waterpipe (WP) and Chewing Tobacco (CT) are two such alternative forms, finding their way into many populations. However, the burden of these alternative forms of tobacco and their socio demographic determinants are still unclear. We assessed the prevalence of WP and CT use among women of reproductive age group in Pakistan. Methods: Data from the most recent Pakistan Demographic and Health Survey 2012–13 (n = 13,558) was used for this analysis. Information obtained from ever married women, aged between 15 and 49 years were analyzed using two separate data subgroups; exclusive WP smokers (total n = 12,995) and exclusive CT users (total n = 12,771). Univariate and Multivariate logistic regression analyses were conducted and results were reported as crude and adjusted Odds Ratio with 95 % confidence intervals. Results: Prevalence of WP smoking and CT were 4 % and 2 %, respectively. After multivariate adjustments, ever married women who were: older than 35 years (OR; 4.68 95 % CI, 2.62–8.37), were poorest (OR = 4.03, 95 % CI 2.08–7.81), and had no education (OR = 9.19, 95 % CI 5.10–16.54), were more likely to be WP smokers. Similarly, ever married women who were: older than 35 years (OR = 3.19, 95 % CI 1.69–6.00), had no education (OR = 4.94, 95 % CI 2.62–9.33), were poor (OR = 1.64, 95 % CI 1.07–2.48) and had visited health facility in last 12 months (OR = 1.81, 95 % CI 1.22–2.70) were more likely to be CT users as well. Conclusion: Older women with lower socio-economic profile were more likely to use WP and CT. Focused policies aiming towards reducing the burden of alternate forms of tobacco use among women is urgently needed to control the tobacco epidemic in the country