This paper addresses the problem of 3D human pose and shape estimation from a single image. Previous approaches consider a parametric model of the human body, SMPL, and attempt to regress the model parameters that give rise to a mesh consistent with image evidence. This parameter regression has been a very challenging task, with model-based approaches underperforming compared to nonparametric solutions in terms of pose estimation. In our work, we propose to relax this heavy reliance on the model's parameter space. We still retain the topology of the SMPL template mesh, but instead of predicting model parameters, we directly regress the 3D location of the mesh vertices. This is a heavy task for a typical network, but our key insight is that the regression becomes significantly easier using a Graph-CNN. This architecture allows us to explicitly encode the template mesh structure within the network and leverage the spatial locality the mesh has to offer. Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location. Having recovered the complete 3D geometry of the mesh, if we still require a specific model parametrization, this can be reliably regressed from the vertices locations. We demonstrate the flexibility and the effectiveness of our proposed graph-based mesh regression by attaching different types of features on the mesh vertices. In all cases, we outperform the comparable baselines relying on model parameter regression, while we also achieve state-of-the-art results among model-based pose estimation approaches.

Convolutional Mesh Regression for Single-Image Human Shape Reconstruction

A fast and accurate circle detection algorithm based on random sampling

A ROS framework for the extrinsic calibration of intelligent vehicles: A multi-sensor, multi-modal approach

We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.

Iterative K-Closest Point Algorithms for Colored Point Cloud Registration.

Deep learning (DL) techniques like recurrent neural networks (RNN) and convolutional neural networks (CNN) are currently being utilised to improve management tooling and workflow classification to increase operational effectiveness. Reliability could be increased, but because of CNN's intricacy, actual research is therefore limited. A brand-new DL structure is suggested in this study to incorporate the visualization of mappings (IVM) within Masked R-CNN. During the first approach, this paradigm, IVM-CNN combines the best features of both approaches, including (1) IVM for object tracking by emphasizing geospatial data for sector recommendations and (2) CNN for machine vision by relying on data for picture categorization. Using spatial and temporal statistics along with visual functionalities, the said approach is tested on M2CAI 2016 contest sets of data, outperforming all prior creations and accomplishing futuristic outcomes to 97.1 mAP for device diagnosis and 96.9 mean rate. It also performs at 50 FPS, which is ten times quicker than region-based CNN. Masked R-CNN substitutes the region proposal network (RPN) with a region proposal module (RPM), which more precisely generates boundary boxes and reduces the demand for labeling. Microsoft HoloLens software is also being generated to offer an augmented reality (AR) stationed approach for clinical education and help. 

Artificial Neural Networks in Healthcare for Augmented Reality

RGB-Depth (RGB-D) cameras are widely used in computer vision and robotics applications such as 3D modeling and human–computer interaction To capture 3D information of an object from different viewpoints simultaneously, we need to use multiple RGB-D cameras To minimize costs, the cameras are often sparsely distributed without shared scene features Due to the advantage of being visible from different viewpoints, spherical objects have been used for extrinsic calibration of widely-separated cameras Assuming that the projected shape of the spherical object is circular, this paper presents a multi-cue-based method for detecting circular regions in a single color image Experimental comparisons with existing methods show that our proposed method accurately detects spherical objects with cluttered backgrounds under different illumination conditions The circle detection method is then applied to extrinsic calibration of multiple RGB-D cameras, for which we propose to use robust cost functions to reduce errors due to misdetected sphere centers Through experiments, we show that the proposed method provides accurate calibration results in the presence of outliers and performs better than a least-squares-based method

Multi-Cue-Based Circle Detection and Its Application to Robust Extrinsic Calibration of RGB-D Cameras

RGBD cameras provide color and depth images at a high speed, so they are becoming increasingly important in many computer vision applications. With the color and depth information, it is easy to build a textured 3D model of a scene without effort on correspondence search. To combine depth data from multiple RGBD cameras in a unified coordinate system, the cameras need to be extrinsically calibrated based on 3D correspondences of scene features. To maximize the scene features simultaneously observed from different viewpoints, a spherical calibration object is widely used. In this paper, we propose a method for automatically detecting a spherical object in an RGB image. Since the detection relies only on RGB information, the proposed method has a potential to be extended to conventional multi-view calibration. Experimental results show that the proposed method accurately detects spherical objects in a cluttered environment.

Automatic sphere detection for extrinsic calibration of multiple RGBD cameras

Three-dimensional human shape reconstruction is important in many applications, such as virtual or augmented reality (VR/AR), virtual clothing fitting, and healthcare. In this paper, we propose a learning-based method for reconstructing a whole-body point cloud from a single front-view human-depth image. Because actual depth images typically suffer from noise and missing data, an accurate point cloud cannot be reasonably obtained by simply predicting a back-view depth image. To solve this problem, we propose to use convolutional neural networks that not only predict a back-view depth image but also refine the input front-view depth image. To train the networks, we propose a carefully designed method for generating synthetic but realistic human-depth images with noise and missing data. Experiments show that the proposed method is effective for obtaining seamless whole-body point clouds. In addition, the experiments show that the networks trained on the synthetic depth images are ready for application to actual depth images.

/pdf/cnn-based-denoising-completion-and-prediction-of-whole-body-3j9rbro5rt.pdf

CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images

Realistic personalized avatars can play an important role in social interactions in virtual reality, increasing body ownership, presence, and dominance. A simple way to obtain the texture of an avatar is to use a single front-view image of a human and to generate the hidden back-view image. The realism of the generated image is crucial in improving the overall texture quality, and subjective image quality assessment methods can play an important role in the evaluation. The subjective methods, however, require dozens of human assessors, a controlled environment, and time. This paper proposes a deep learning-based image reality assessment method, which is fully automatic and has a short testing time of nearly a quarter second per image. We train various discriminators to predict whether an image is real or generated. The trained discriminators are then used to give a mean opinion score for the reality of an image. Through experiments on human back-view images, we show that our learning-based mean opinion scores are close to their subjective counterparts in terms of the root mean square error between them.

https://www.mdpi.com/2079-9292/9/4/656/pdf

Feasibility Analysis of Deep Learning-Based Reality Assessment of Human Back-View Images

There have been significant advancements in dynamic novel view synthesis in recent years. However, current deep learning models often require (1) prior models (e.g., SMPL human models), (2) heavy pre-processing, or (3) per-scene optimization. We propose to utilize RGBD cameras to remove these limitations and synthesize free-viewpoint videos of dynamic indoor scenes. We generate feature point clouds from RGBD frames and then render them into free-viewpoint videos via a neural renderer. However, the inaccurate, unstable, and incomplete depth measurements induce severe distortions, flickering, and ghosting artifacts. We enforce spatial-temporal consistency via the proposed Cycle Reconstruction Consistency and Temporal Stabilization module to reduce these artifacts. We introduce a simple Regional Depth-Inpainting module that adaptively inpaints missing depth values to render complete novel views. Additionally, we present a Human-Things Interactions dataset to validate our approach and facilitate future research. The dataset consists of 43 multi-view RGBD video sequences of everyday activities, capturing complex interactions between human subjects and their surroundings. Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency. We will release our code, and the dataset on the website soon.

Learning Dynamic View Synthesis With Few RGBD Cameras

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