In recent years, there have been a variety of proposals for high quality 3D reconstruction by fusion of depth and normal maps that contain good low and high frequency information respectively. Typically, these methods create an initial mesh representation of the complete object or scene being scanned. Subsequently, normal estimates are assigned to each mesh vertex and a mesh-normal fusion step is carried out. In this paper, we present a complete pipeline for such depth-normal fusion. The key innovations in our pipeline are twofold. Firstly, we introduce a global multi-view non-rigid refinement step that corrects for the non-rigid misalignment present in the depth and normal maps. We demonstrate that such a correction is crucial for preserving fine-scale 3D features in the final reconstruction. Secondly, despite adequate care, the averaging of multiple normals invariably results in blurring of 3D detail. To mitigate this problem, we propose an approach that selects one out of many available normals. Our global cost for normal selection incorporates a variety of desirable properties and can be efficiently solved using graph cuts. We demonstrate the efficacy of our approach in generating high quality 3D reconstructions of both synthetic and real 3D models and compare with existing methods in the literature.

The increased availability of point cloud data in recent years has lead to a concomitant requirement for high quality denoising methods. This is particularly the case with data obtained using depth cameras or from multi-view stereo reconstruction as both approaches result in noisy point clouds and include significant outliers. Most of the available denoising methods in the literature are not sufficiently robust to outliers and/or are unable to preserve fine-scale 3D features in the denoised representations. In this paper we propose an approach to point cloud denoising that is both robust to outliers and capable of preserving fine-scale 3D features. We identify and remove outliers by utilising a dissimilarity measure based on point positions and their corresponding normals. Subsequently, we use a robust approach to estimate surface point positions in a manner designed to preserve sharp and fine-scale 3D features. We demonstrate the efficacy of our approach and compare with similar methods in the literature by means of experiments on synthetic and real data including large-scale 3D reconstructions of heritage monuments.

We propose a novel global 3D mesh denoising method that is carried out in two steps, i.e. Mollification of normals followed by vertex correction. Both steps involve minimising sparse, quadratic cost functions that yield efficient non-iterative solutions. In the mollification step, we use adaptive weights that allows for appropriate diffusion while preserving features. While many existing methods only correct for the vertex position along the normal direction, we argue that this is inadequate in many scenarios. Instead, we allow for vertex correction in all directions while enforcing a novel face fairness penalty that preserves face shapes in the denoised mesh. We present a number of tests and examples that demonstrate the efficacy of our method in denoising while preserving face fairness. We demonstrate the superiority of our approach over some relevant methods in the literature.

In this paper we present a depth-guided photometric 3D reconstruction method that works solely with a depth camera like the Kinect. Existing methods that fuse depth with normal estimates use an external RGB camera to obtain photometric information and treat the depth camera as a black box that provides a low quality depth estimate. Our contribution to such methods are two fold. Firstly, instead of using an extra RGB camera, we use the infra-red (IR) camera of the depth camera system itself to directly obtain high resolution photometric information. We believe that ours is the first method to use an IR depth camera system in this manner. Secondly, photometric methods applied to complex objects result in numerous holes in the reconstructed surface due to shadows and self-occlusions. To mitigate this problem, we develop a simple and effective multiview reconstruction approach that fuses depth and normal information from multiple viewpoints to build a complete, consistent and accurate 3D surface representation. We demonstrate the efficacy of our method to generate high quality 3D surface reconstructions for some complex 3D figurines.

The problem of human detection is challenging, more so, when faced with adverse conditions such as occlusion and background clutter. This paper addresses the problem of human detection by representing an extracted feature of an image using a sparse linear combination of chosen dictionary atoms. The detection along with the scale finding, is done by using the coefficients obtained from sparse representation. This is of particular interest as we address the problem of scale using a scale-embedded dictionary where the conventional methods detect the object by running the detection window at all scales.