Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression

Not all the human body parts have the same degree of freedom (DOF) due to the physiological structure. For example, the limbs may move more flexibly and freely than the torso does. Most of the existing 3D pose estimation methods, despite the very promising results achieved, treat the body joints equally and consequently often lead to larger reconstruction errors on the limbs. In this paper, we propose a progressive approach that explicitly accounts for the distinct DOFs among the body parts. We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated. Meanwhile, the high-DOF parts may, in turn, impose a constraint on where the low-DOF ones lie. As a result, parts with different DOFs supervise one another, yielding physically constrained and plausible pose-estimation results. To further facilitate the prediction of the high-DOF parts, we introduce a pose-attribution estimation, where the relative location of a limb joint with respect to the torso, which has the least DOF of a human body, is explicitly estimated and further fed to the joint-estimation module. The proposed approach achieves very promising results, outperforming the state of the art on several benchmarks.

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate datasets. Existent ones are usually collected in indoor controlled environments where motion capture systems are used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations. These images are generated from 200 viewpoints among which 70 challenging extreme viewpoints. This data was created starting from high resolution textured 3D body scans and by incorporating various realistic backgrounds. Retraining a state-of-the-art 3D pose estimation approach using data augmented with 3DBodyTex.Pose showed promising improvement in the overall performance, and a sensible decrease in the per joint position error when testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the research community with new possibilities for generalizing 3D pose estimation from monocular in-the-wild images.

6D pose estimation is an important branch in the field of vision measurement, and is widely used in the fields of robotics, autonomous driving and reality augmentation. The latest research trend in 6D pose estimation is to train a deep neural network to directly predict the 2D projection position of the 3D keypoint from the image, establish the corresponding relationship, and finally use the perspective-n-point (PnP) algorithm to perform pose estimation. The current challenge of pose estimation is that when objects are textureless, occluded or scene-clutterd, the detection accuracy is reduced, and most of the existing algorithm models are large and cannot accommodate real-time requirements. In this paper, we introduce a densely connected feature pyramid network (DFPN) that can efficiently integrate and utilize features. We combine the cross-stage partial network (CSPNet) with DFPN to design a new network for 6D pose estimation, DFPN-6D, a new approach for 6D object pose estimation. DFPN-6D can efficiently and accurately handle objects with textureless, occluded and scene clutter and estimate their full 6D poses in a single shot. Furthermore, we propose a new confidence calculation method and loss function for object pose estimation, which can fully consider spatial information. Finally, we propose a novel augmentation method for direct 6D pose estimation approaches to improve performance and generalization ability in the case of occlusion, which is called 6D augmentation. Our approach achieves a new state-of-the-art accuracy of 98.06 and 87.09 in terms of the ADD(-S) metric on the Linemod dataset and Occluded-Linemod dataset, and our method also achieves the best result in terms of the different metric on the MULT-I dataset, BIN-P dataset and T-LESS dataset, respectively, while still running end-to-end at over 65 FPS. The experimental results demonstrate that our algorithm is robust to textureless materials and occlusion while running more efficiently than other methods. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose.

With the advantages of wide range, non-contact and high flexibility, the visual estimation technology of target pose has been widely applied in modern industry, robot guidance and other engineering practices. However, due to the influence of complicated industrial environment, outside interference factors, lack of object characteristics, restrictions of camera and other limitations, the visual estimation technology of target pose is still faced with many challenges. Focusing on the above problems, a pose estimation method of the industrial objects is developed based on 3D models of targets. By matching the extracted shape characteristics of objects with the priori 3D model database of targets, the method realizes the recognition of target. Thus a pose estimation of objects can be determined based on the monocular vision measuring model. The experimental results show that this method can be implemented to estimate the position of rigid objects based on poor images information, and provides guiding basis for the operation of the industrial robot.

Many approaches have been proposed for human pose estimation in single and multi-view RGB images. However, some environments, such as the operating room, are still very challenging for state-of-the-art RGB methods. In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features. The proposed method permits the joint detection and estimation of the poses without knowing a priori the number of persons present in the scene. We evaluate this approach on a novel multi-view RGB-D dataset acquired during live surgeries and annotated with ground truth 3D poses.

Weakly-supervised pre-training for 3D human pose estimation via perspective knowledge

In the presented work, innovative low-sampling lightweight architecture is proposed to solve the task of 3D human pose estimation based on 2D key points. The approach introduces specialized trainable pose encodings designed for 3D pose estimation tasks, which are used in conjunction with traditional pose encodings to represent the input data. The architecture of the method includes multilevel feature processing and their adaptive association using a spatial attention mechanism, which allows to enhance relevant features. Experiments conducted on standard test datasets confirmed the effectiveness of the proposed method: a mean joint position error (MPJPE) value of 42.1 was achieved, which exceeds the results of existing approaches.

Deep 3D human pose estimation: A review

Generalization is very important for pose estimation, especially for 3D pose estimation where small changes in the 2D images could trigger structural changes in the 3D space. To achieve generalization, the system needs to have the capability of detecting estimation errors by double-checking the projection coherence between the 3D and 2D spaces and adapting its network inference process based on this feedback. Current pose estimation is one-time feed-forward and lacks the capability to gather feedback and adapt the inference outcome. To address this problem, we propose to explore the concept of progressive inference where the network learns an observer to continuously detect the prediction error based on constraints matching, as well as an adjuster to refine its inference outcome based on these constraints errors. Within the context of 3D hand pose estimation, we find that this observer-adjuster design is relatively unstable since the observer is operating in the 2D image domain while the adjuster is operating in the 3D domain. To address this issue, we propose to construct two sets of observers-adjusters with complementary constraints from different perspectives. They operate in a dynamic sequential manner controlled by a decision network to progressively improve the 3D pose estimation. We refer to this method as Cross-Constrained Progressive Inference (CCPI). Our extensive experimental results on FreiHAND and HO-3D benchmark datasets demonstrate that the proposed CCPI method is able to significantly improve the generalization capability and performance of 3D hand pose estimation.

3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human–robot interaction, surveillance systems or sports, among many others. However, several obstacles such as occlusions, random camera perspectives, or the scarcity of 3D labelled data, have been hampering the models’ performance and limiting their deployment in real-world scenarios. The higher availability of cameras has led researchers to explore multi-view solutions due to the advantage of being able to exploit different perspectives to reconstruct the pose. Most existing reviews focus mainly on monocular 3D human pose estimation and a comprehensive survey only on multi-view approaches to determine the 3D pose has been missing since 2012. Thus, the goal of this survey is to fill that gap and present an overview of the methodologies related to 3D pose estimation in multi-view settings, understand what were the strategies found to address the various challenges and also, identify their limitations. According to the reviewed articles, it was possible to find that most methods are fully-supervised approaches based on geometric constraints. Nonetheless, most of the methods suffer from 2D pose mismatches, to which the incorporation of temporal consistency and depth information have been suggested to reduce the impact of this limitation, besides working directly with 3D features can completely surpass this problem but at the expense of higher computational complexity. Models with lower supervision levels were identified to overcome some of the issues related to 3D pose, particularly the scarcity of labelled datasets. Therefore, no method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Due to the existing trade-off between complexity and performance, the best method depends on the application scenario. Therefore, further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost. To this goal, techniques such as active learning, methods that learn with a low level of supervision, the incorporation of temporal consistency, view selection, estimation of depth information and multi-modal approaches might be interesting strategies to keep in mind when developing a new methodology to solve this task. • First review only on multi-view, multi-modal methods to estimate 3D pose since 2012. • Multi-view allows capturing the full body geometry, making 3D pose estimation easier. • Real-world applications include sports, broadcasting, rehabilitation or animation. • Finding a fast, accurate method with low computational cost remains a challenge. • Multi-modal methods or view selection can lead to an efficient and effective model.

Rich geometric understanding of the world is an important component of many robotic applications such as planning and manipulation. In this paper, we present a modular pipeline for pose and shape estimation of objects from RGB-D images given their category. The core of our method is a generative shape model, which we integrate with a novel initialization network and a differentiable renderer to enable 6D pose and shape estimation from a single or multiple views. We investigate the use of discretized signed distance fields as an efficient shape representation for fast analysis-by-synthesis optimization. Our modular framework enables multi-view optimization and extensibility. We demonstrate the benefits of our approach over state-of-the-art methods in several experiments on both synthetic and real data. We open-source our approach at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/roym899/sdfest</uri> .

In this paper we describe an approach for detection and pose estimation of colored objects with only few or no textural features. The approach consists of two separate stages. First, we perform vision-based object detection and hypothesis filtering. Then, we estimate and validate the object's pose in 3-D laser scans. For object detection we integrate image segmentation results from multiple viewpoints in a set-theoretical filter that provides a probabilistically sound estimate of the number of objects and their respective locations. For validation and pose estimation we search for the best pose by sampling from a geometric measurement model. The system has been validated during autonomous exploration missions in unstructured and space-like environments.

A pose (relative attitude and position) estimation approach to support proximity operations such as autonomous landing of unmanned air vehicles or spacecraft on-orbit servicing is investigated in this paper. An algorithm is developed based on a feature detection capability using the point cloud data that a flash lidar (light detection and ranging) produces. The feature refers to an edge, a corner, or a distinct geometrical shape of an object. Once features are detected, orthogonal triads that are attached to the object are constructed using them. Pose estimation is then performed based on the orientation of the triads with respect to the lidar sensor frame. Since the algorithm employs detected features, it allows pose estimation of an object from a single set of point cloud data. In contrast, the conventional iterative closest point based algorithms typically require two sets of point cloud data. The estimated pose thus represents the changes in pose between the two data sets, leaving the pose of the object with respect to the sensor still to be determined. This featurebased pose estimation capability performs directly an initial pose acquisition with respect to the sensor. The algorithm may also help reduce the number of data points required for pose estimation since a small, selected feature of an object within the sensor field of view can be employed to estimate the pose. Employing the algorithm developed, pose estimation tests were conducted and their preliminary results are presented, which were obtained using a flash lidar in a laboratory testing environment.

SkeletonPose: Exploiting human skeleton constraint for 3D human pose estimation

State-of-the-art object pose estimation handles multiple instances in a test image by using multi-model formulations: detection as a first stage and then separately trained networks per object for 2D-3D geometric correspondence prediction as a second stage. Poses are subsequently estimated using the Perspective-n-Points algorithm at runtime. Unfortunately, multi-model formulations are slow and do not scale well with the number of object instances involved. Recent approaches show that direct 6D object pose estimation is feasible when derived from the aforementioned geometric correspondences. We present an approach that learns an intermediate geometric representation of multiple objects to directly regress 6D poses of all instances in a test image. The inherent end-to-end trainability overcomes the requirement of separately processing individual object instances. By calculating the mutual Intersection-over-Unions, pose hypotheses are clustered into distinct instances, which achieves negligible runtime overhead with respect to the number of object instances. Results on multiple challenging standard datasets show that the pose estimation performance is superior to single-model state-of-the-art approaches despite being more than ~35 times faster. We additionally provide an analysis showing real-time applicability (> 24 fps) for images where more than 90 object instances are present. Further results show the advantage of supervising geometric correspondence-based object pose estimation with the 6D pose.