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

Doctoral thesis (2016)

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 ... [more ▼]

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. [less ▲]

RGB-D Multi-View System Calibration for Full 3D Scene Reconstruction

in 22nd International Conference on Pattern Recognition (ICPR'14) (2014)

One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We ... [more ▼]

One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We propose BAICP+ which combines Bundle Adjustment (BA) and Iterative Closest Point (ICP) algorithms to take into account both 2D visual and 3D shape information in one minimization formulation to estimate relative pose parameters of each camera. BAICP+ is generic enough to take different types of visual features into account and can be easily adapted to varying quality of 2D and 3D data. We perform experiments on real and simulated data. Results show that with the right weighting factor BAICP+ has an optimal performance when compared to BA and ICP used independently or sequentially. [less ▲]