Unsupervised Monocular Depth and Pose Estimation Using Multiple Masks Based on Photometric and Geometric Consistency

2020 ◽  
Author(s):  
Huifang Kong ◽  
Tiankuo Liu ◽  
Jie Hu ◽  
Yao Fang ◽  
Jixing Sun
Keyword(s):  
Pose Estimation ◽  
Geometric Consistency ◽  
Monocular Depth

scholarly journals Monocular weakly supervised depth and pose estimation method based on multi-information fusion

2021 ◽  
Author(s):  
Zhimin Zhang ◽  
◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
◽  
...  
Keyword(s):  
Information Fusion ◽  
Pose Estimation ◽  
Video Sequence ◽  
Stereo Matching ◽  
Estimation Method ◽  
Ground Truth ◽  
Estimation Accuracy ◽  
Monocular Video ◽  
Monocular Depth ◽  
Weakly Supervised

The depth and pose information are the basic issues in the field of robotics, autonomous driving, and virtual reality, and are also the focus and difficult issues of computer vision research. The supervised monocular depth and pose estimation learning are not feasible in environments where labeled data is not abundant. Self-supervised monocular video methods can learn effectively only by applying photometric constraints without expensive ground true depth label constraints, which results in an inefficient training process and suboptimal estimation accuracy. To solve these problems, a monocular weakly supervised depth and pose estimation method based on multi-information fusion is proposed in this paper. First, we design a high-precision stereo matching method to generate a depth and pose data as the "Ground Truth" labels to solve the problem that the ground truth labels are difficult to obtain. Then, we construct a multi-information fusion network model based on the "Ground truth" labels, video sequence, and IMU information to improve the estimation accuracy. Finally, we design the loss function of supervised cues based on "Ground Truth" labels cues and self-supervised cues to optimize our model. In the testing phase, the network model can separately output high-precision depth and pose data from a monocular video sequence. The resulting model outperforms mainstream monocular depth and poses estimation methods as well as the partial stereo matching method in the challenging KITTI dataset by only using a small number of real training data(200 pairs).

Blood Flow Pattern and Anastomotic Compliance for Interrupted versus Continuous Coronary Bypass Grafts

2005 ◽  
Vol 6 (2) ◽  
pp. 65 ◽  
Author(s):  
Marc Gerdisch ◽  
Thomas Hinkamp ◽  
Stephen D. Ainsworth
Keyword(s):  
Blood Flow ◽  
Vessel Wall ◽  
Coronary Bypass ◽  
Bypass Grafts ◽  
Cross Sectional ◽  
Coronary Bypass Grafts ◽  
Geometric Consistency ◽  
Suturing Technique ◽  
Suturing Techniques ◽  
Key Indicators

<P>Background: Use of the interrupted coronary anastomosis has largely been abandoned in favor of the more rapid continuous suturing technique. The Coalescent U-CLIP anastomotic device allows the surgeon to create an interrupted distal anastomosis in the same amount of time that it would take to create a continuous anastomosis. This acute bovine study examined the effect of the anastomotic technique on blood flow and vessel wall function. </P><P>Methods: End-to-side coronary anastomoses were created in an open chest bovine model using the left and right internal thoracic arteries and the left anterior descending coronary artery. All other variables except suturing technique were carefully controlled. In each animal, one anastomosis was completed using a continuous suturing technique and the other was performed in an interrupted fashion using the Coalescent U-CLIP anastomotic device. Volumetric flow curves through each graft were analyzed using key indicators of anastomotic quality, and anastomotic compliance was evaluated using intravascular ultrasound. Luminal castings were created of each vessel to examine the interior surface of each anastomosis for constrictions and deformities. </P><P>Results: The interrupted anastomoses created with the Coalescent U-CLIP anastomotic device showed significant differences with respect to anastomotic compliance, pulsatility index, peak flow, and percentage of diastolic flow. The cross-sectional area and degree of luminal deformity were also different for the two suturing techniques. </P><P>Conclusions: In this acute bovine model, interrupted coronary anastomoses demonstrated superior geometric consistency and greater physiologic compliance than did continuously sutured anastomoses. The interrupted anastomosis also caused fewer disturbances to the flow waveform, behaving similarly to a normal vessel wall. The combination of these effects may influence both acute and long-term patency of the coronary bypass grafts.</P>

EFFECT OF OBSERVER MOVEMENT ON MONOCULAR DEPTH PERCEPTION

1967 ◽  
Vol 24 (3) ◽  
pp. 725-726 ◽  
Author(s):  
GORDON M. REDDING ◽  
ROY B. MEFFERD ◽  
BETTY A. WIELAND
Keyword(s):  
Depth Perception ◽  
Monocular Depth ◽  
Observer Movement

3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

2020 ◽  
Author(s):  
Gopi Krishna Erabati
Keyword(s):  
Pose Estimation ◽  
Autonomous Systems ◽  
Microsoft Kinect ◽  
Object Localization ◽  
Initial Alignment ◽  
Relative Localization ◽  
3D Data ◽  
Industry Automation ◽  
Ofthe Object ◽  
3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

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