Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations

We propose a novel approach for enhancing depth videos containing non-rigidly deforming objects. Depth sensors are capable of capturing depth maps in real-time but suffer from high noise levels and low spatial resolutions. While solutions for reconstructing 3D details in static scenes, or scenes with rigid global motions have been recently proposed, handling unconstrained non-rigid deformations in relative complex scenes remains a challenge. Our solution consists in a recursive dynamic multi-frame superresolution algorithm where the relative local 3D motions between consecutive frames are directly accounted for. We rely on the assumption that these 3D motions can be decoupled into lateral motions and radial displacements. This allows to perform a simple local per-pixel tracking where both depth measurements and deformations are dynamically optimized. The geometric smoothness is subsequently added using a multi-level L1 minimization with a bilateral total variation regularization. The performance of this method is thoroughly evaluated on both real and synthetic data. As compared to alternative approaches, the results show a clear improvement in reconstruction accuracy and in robustness to noise, to relative large non-rigid deformations, and to topological changes. Moreover, the proposed approach, implemented on a CPU, is shown to be computationally efficient and working in real-time

Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly real-time, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm.Comment: submitted to IROS 201

Super-Resolution Approaches for Depth Video Enhancement

Sensing using 3D technologies has seen a revolution in the past years where cost-effective depth sensors are today part of accessible consumer electronics. Their ability in directly capturing depth videos in real-time has opened tremendous possibilities for multiple applications in computer vision. These sensors, however, have major shortcomings due to their high noise contamination, including missing and jagged measurements, and their low spatial resolutions. In order to extract detailed 3D features from this type of data, a dedicated data enhancement is required. We propose a generic depth multi–frame super–resolution framework that addresses the limitations of state-of-theart depth enhancement approaches. The proposed framework doesnot need any additional hardware or coupling with different modalities. It is based on a new data model that uses densely upsampled low resolution observations. This results in a robust median initial estimation, further refined by a deblurring operation using a bilateraltotal variation as the regularization term. The upsampling operation ensures a systematic improvement in the registration accuracy. This is explored in different scenarios based on the motions involved in the depth video. For the general and most challenging case of objects deforming non-rigidly in full 3D, we propose a recursive dynamic multi–frame super-resolution algorithm where the relative local 3D motions between consecutive frames are directly accounted for. We rely on the assumption that these 3D motions can be decoupled into lateral motions and radial displacements. This allows to perform a simple local per–pixel tracking where both depth measurements and deformations are optimized. As compared to alternative approaches, the results show a clear improvement in reconstruction accuracy and in robustness to noise, to relative large non-rigid deformations, and to topological changes. Moreover, the proposed approach, implemented on a CPU, is shown to be computationally efficient and working in real-time

Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data

Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-art accuracy on public benchmarks but are restricted to specific types of objects and motions covered by the training datasets. Model-based approaches do not rely on training data but show lower accuracy on these datasets. In this paper, we introduce a model-based method called Structure from Articulated Motion (SfAM), which can recover multiple object and motion types without training on extensive data collections. At the same time, it performs on par with learning-based state-of-the-art approaches on public benchmarks and outperforms previous non-rigid structure from motion (NRSfM) methods. SfAM is built upon a general-purpose NRSfM technique while integrating a soft spatio-temporal constraint on the bone lengths. We use alternating optimization strategy to recover optimal geometry (i.e., bone proportions) together with 3D joint positions by enforcing the bone lengths consistency over a series of frames. SfAM is highly robust to noisy 2D annotations, generalizes to arbitrary objects and does not rely on training data, which is shown in extensive experiments on public benchmarks and real video sequences. We believe that it brings a new perspective on the domain of monocular 3D recovery of articulated structures, including human motion capture.Comment: 21 pages, 8 figures, 2 table

Full 3D Reconstruction of Non-Rigidly Deforming Objects

In this article, we discuss enhanced full 360° 3D reconstruction of dynamic scenes containing non-rigidly deforming objects using data acquired from commodity depth or 3D cameras. Several approaches for enhanced and full 3D reconstruction of non-rigid objects have been proposed in the literature. These approaches suffer from several limitations due to requirement of a template, inability to tackle large local deformations and topology changes, inability to tackle highly noisy and low-resolution data, and inability to produce online results. We target online and template-free enhancement of the quality of noisy and low-resolution full 3D reconstructions of dynamic non-rigid objects. For this purpose, we propose a view-independent recursive and dynamic multi-frame 3D super-resolution scheme for noise removal and resolution enhancement of 3D measurements. The proposed scheme tracks the position and motion of each 3D point at every timestep by making use of the current acquisition and the result of the previous iteration. The effects of system blur due to per-point tracking are subsequently tackled by introducing a novel and efficient multi-level 3D bilateral total variation regularization. These characteristics enable the proposed scheme to handle large deformations and topology changes accurately. A thorough evaluation of the proposed scheme on both real and simulated data is carried out. The results show that the proposed scheme improves upon the performance of the state-of-the-art methods and is able to accurately enhance the quality of low-resolution and highly noisy 3D reconstructions while being robust to large local deformations.</jats:p

FULL 3D RECONSTRUCTION OF DYNAMIC NON-RIGID SCENES: ACQUISITION AND ENHANCEMENT

Recent advances in commodity depth or 3D sensing technologies have enabled us to move closer to the goal of accurately sensing and modeling the 3D representations of complex dynamic scenes. Indeed, in domains such as virtual reality, security, surveillance and e-health, there is now a greater demand for aff ordable and flexible vision systems which are capable of acquiring high quality 3D reconstructions. Available commodity RGB-D cameras, though easily accessible, have limited fi eld-of-view, and acquire noisy and low-resolution measurements which restricts their direct usage in building such vision systems. This thesis targets these limitations and builds approaches around commodity 3D sensing technologies to acquire noise-free and feature preserving full 3D reconstructions of dynamic scenes containing, static or moving, rigid or non-rigid objects. A mono-view system based on a single RGB-D camera is incapable of acquiring full 360 degrees 3D reconstruction of a dynamic scene instantaneously. For this purpose, a multi-view system composed of several RGB-D cameras covering the whole scene is used. In the first part of this thesis, the domain of correctly aligning the information acquired from RGB-D cameras in a multi-view system to provide full and textured 3D reconstructions of dynamic scenes, instantaneously, is explored. This is achieved by solving the extrinsic calibration problem. This thesis proposes an extrinsic calibration framework which uses the 2D photometric and 3D geometric information, acquired with RGB-D cameras, according to their relative (in)accuracies, a ffected by the presence of noise, in a single weighted bi-objective optimization. An iterative scheme is also proposed, which estimates the parameters of noise model aff ecting both 2D and 3D measurements, and solves the extrinsic calibration problem simultaneously. Results show improvement in calibration accuracy as compared to state-of-art methods. In the second part of this thesis, the domain of enhancement of noisy and low-resolution 3D data acquired with commodity RGB-D cameras in both mono-view and multi-view systems is explored. This thesis extends the state-of-art in mono-view template-free recursive 3D data enhancement which targets dynamic scenes containing rigid-objects, and thus requires tracking only the global motions of those objects for view-dependent surface representation and fi ltering. This thesis proposes to target dynamic scenes containing non-rigid objects which introduces the complex requirements of tracking relatively large local motions and maintaining data organization for view-dependent surface representation. The proposed method is shown to be e ffective in handling non-rigid objects of changing topologies. Building upon the previous work, this thesis overcomes the requirement of data organization by proposing an approach based on view-independent surface representation. View-independence decreases the complexity of the proposed algorithm and allows it the flexibility to process and enhance noisy data, acquired with multiple cameras in a multi-view system, simultaneously. Moreover, qualitative and quantitative experimental analysis shows this method to be more accurate in removing noise to produce enhanced 3D reconstructions of non-rigid objects. Although, extending this method to a multi-view system would allow for obtaining instantaneous enhanced full 360 degrees 3D reconstructions of non-rigid objects, it still lacks the ability to explicitly handle low-resolution data. Therefore, this thesis proposes a novel recursive dynamic multi-frame 3D super-resolution algorithm together with a novel 3D bilateral total variation regularization to filter out the noise, recover details and enhance the resolution of data acquired from commodity cameras in a multi-view system. Results show that this method is able to build accurate, smooth and feature preserving full 360 degrees 3D reconstructions of the dynamic scenes containing non-rigid objects