Monocular Depth from Small Motion Video Accelerated

Ham, Christopher C. W.; Chang, Ming-Fang; Lucey, Simon; Singh, Surya P. N.

doi:10.1109/3dv.2017.00071

Cited by 10 publications

(17 citation statements)

References 15 publications

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“…However, the UAV motion is usually relatively small, and the obtained video should have a small baseline, thus having similar camera parameters between two consecutive frames. In this case, applying the bundle adjustment to refine those parameters will result in significant errors (Ha et al., 2016; Im, Ha, Choe, Jeon, & Kweon, 2015; Ham, Chang, Lucey, & Singh, 2017). Having large camera motion could possibly address this issue during indoor measurement (Yoon et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles

Weng

Shan

et al. 2020

Computer aided Civil Eng

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Structural displacement is an important quantity to assess the health of civil infrastructure. Vision‐based approaches using unmanned aerial vehicles (UAV) mounted with high‐resolution cameras have been proposed for this purpose. However, because the camera itself is moving with the UAV, any video obtained will contain both the motion of the structure and the motion of the camera. Planar homography can be used to eliminate the errors induced by the camera movement without the need for camera parameters. However, its direct application to large structures still has limitations, because capturing the undeformed regions, along with the measurement points on the structure, within a single image with sufficient resolution is seldom feasible. In this study, a new framework is presented to address these issues and facilitate the extraction of the structural displacement from videos taken by a UAV‐mounted camera. First, a two‐layer feedforward neural network (FNN) is adopted to obtain the image coordinates of the selected features of the structure on its stationary position, which are further used as homography features. Next, the structural displacement is estimated with the homography transformation matrix determined from the obtained homography features. Finally, the proposed approach is validated on both a six‐story shear‐building model in the laboratory and an elevator tower located in Zhongshan City, China. These results demonstrate the efficacy of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles

Weng

Shan

et al. 2020

Computer aided Civil Eng

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“…We take advantage of the fact that the input is a single continuous video sequence. We use the geometric bundle adjustment based initialization scheme proposed in [24] to get relative pose estimates for an initial baseline distance. Then, a LK tracker is used to track the camera frames in the video, and a keyframe is selected once the camera moves a certain baseline distance.…”

Section: Camera Pose Estimationmentioning

confidence: 99%

High Accuracy Face Geometry Capture using a Smartphone Video

Agrawal¹,

Pahuja²,

Lucey³

2020

Preprint

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“…One research direction that has recently led to renewed interest is the depth estimation from image sequences acquired from narrow/small baseline in the range of about 8mm. This is popularly known as Depth from Small Motion (DfSM), and many research contributions have been made over the years (Yu and Gallup, 2014), (Joshi and Zitnick, 2014), (Ha et al, 2016), (Corcoran and Javidnia, 2017), (Ham et al, 2017). For hand-held cameras, small amount of motion is always present, which can occur accidentally as a re-sult of hand-shaking motion, tremble, source vibration etc.…”

Section: Introductionmentioning

confidence: 99%

Depth from Small Motion using Rank-1 Initialization

Fasogbon

2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Depth from Small Motion (DfSM) (Ha et al., 2016) is particularly interesting for commercial handheld devices because it allows the possibility to get depth information with minimal user effort and cooperation. Due to speed and memory issue on these devices, the self calibration optimization of the method using Bundle Adjustment (BA) need as little as 10-15 images. Therefore, the optimization tends to take many iterations to converge or may not converge at all in some cases. This work propose a robust initialization for the bundle adjustment using the rank-1 factorization method (Tomasi and Kanade, 1992), (Aguiar and Moura, 1999a). We create a constraint matrix that is rank-1 in a noiseless situation, then use SVD to compute the inverse depth values and the camera motion. We only need about quarter fraction of the bundle adjustment iteration to converge. We also propose grided feature extraction technique so that only important and small features are tracked all over the image frames. This also ensure speedup in the full execution time on the mobile device.For the experiments, we have documented the execution time with the proposed Rank-1 initialization on two mobile device platforms using optimized accelerations with CPU-GPU co-processing. The combination of Rank 1-BA generates more robust depth-map and is significantly faster than using BA alone.

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Monocular Depth from Small Motion Video Accelerated

Cited by 10 publications

References 15 publications

Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles

Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles

High Accuracy Face Geometry Capture using a Smartphone Video

Depth from Small Motion using Rank-1 Initialization

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