Object recognition using shape-from-shading

This paper investigates whether surface topography information extracted from intensity images using a recently reported shape-from-shading (SFS) algorithm can be used for the purposes of 3D object recognition. We consider how curvature and shape-index information delivered by this algorithm can be used to recognize objects based on their surface topography. We explore two contrasting object recognition strategies. The first of these is based on a low-level attribute summary and uses histograms of curvature and orientation measurements. The second approach is based on the structural arrangement of constant shape-index maximal patches and their associated region attributes. We show that region curvedness and a string ordering of the regions according to size provides recognition accuracy of about 96 percent. By polling various recognition schemes. including a graph matching method. we show that a recognition rate of 98-99 percent is achievable

3D-reconstruction of human jaw from a single image : integration between statistical shape from shading and shape from shading.

Object modeling is a fundamental problem in engineering, involving talents from computer-aided design, computational geometry, computer vision and advanced manufacturing. The process of object modeling takes three stages: sensing, representation, and analysis. Various sensors may be used to capture information about objects; optical cam- eras and laser scanners are common with rigid objects, while X-ray, CT and MRI are common with biological organs. These sensors may provide a direct or indirect inference about the object, requiring a geometric representation in the computer that is suitable for subsequent usage. Geometric representations that are compact, i.e., capture the main features of the objects with minimal number of data points or vertices, fall into the domain of computational geometry. Once a compact object representation is in the computer, various analysis steps can be conducted, including recognition, coding, transmission, etc. The subject matter of this thesis is object reconstruction from a sequence of optical images. An approach to estimate the depth of the visible portion of the human teeth from intraoral cameras has been developed, extending the classical shape from shading (SFS) solution to non-Lambertian surfaces with known object illumination characteristics. To augment the visible portion, and in order to have the entire jaw reconstructed without the use of CT or MRI or even X-rays, additional information will be added to database of human jaws. This database has been constructed from an adult population with variations in teeth size, degradation and alignments. The database contains both shape and albedo information for the population. Using this database, a novel statistical shape from shading (SSFS) approach has been created. To obtain accurate result from shape from shading and statistical shape from shading, final step will be integrated two approaches (SFS,SSFS) by using Iterative Closest Point algorithm (ICP). Keywords: computer vision, shading, 3D shape reconstruction, shape from shading, statistical, shape from shading, Iterative Closest Point

The effects of shadow presence on visual object-recognition

Shadows provide valuable cues for many aspects of visual perception. This thesis discusses the definitions of cast and attached shadows, and the different types of shadow borders that exist. Eight experiments investigated whether the presence of shadows affects the speed or accuracy of human object recognition performance. Experiments 1 to 4 investigated the contributions of attached shadows to the recognition of novel objects, using a sequential-matching task modelled on that of Tarr, Kersten, and Biilthoff (1998). Their finding, of faster reaction times associated with the presence of shadows, was not replicated. Reaction times were not affected by the presence or absence of shadows. Across the four experiments, discrimination was either unaffected by shadow presence, or was at its highest when there were no shadows present. In Experiments 5 to 7, the effects of cast shadow presence on object recognition were assessed. Visual cues about the shape of the objects were constrained by manipulating the degree of foreshortening of both the objects, and the shadows cast by the objects. Shadow presence was only of benefit to recognition in highly constrained situations: where the objects were severely foreshortened, while their cast shadows were not. Experiment 8 assessed the affect upon recognition of manipulating shape-from-shading cues independently from shadow-border cues. Shadow presence was only beneficial where shading was negligible. It is suggested that shadow presence may only provide observable benefits to object recognition when other cues to an object's identity, such as bounding contour and shape-from-shading, are minimal. It appears that shadows have the potential to facilitate object recognition, but in most situations their presence will not produce any discriminatory, or reaction time, benefit

Effects of lighting on the perception of facial surfaces

The problem of variable illumination for object constancy has been largely neglected by "edge-based" theories of object recognition. However, there is evidence that edge-based schemes may not be sufficient for face processing and that shading information may be necessary (Bruce. 1988). Changes in lighting affect the pattern of shading on any three-dimensional object and the aim of this thesis was to investigate the effects of lighting on tasks involving face perception. Effects of lighting are first reported on the perception of the hollow face illusion (Gregory, 1973). The impression of a convex face was found to be stronger when light appeared to be from above, consistent with the importance of shape-from- shading which is thought to incorporate a light-from-above assumption. There was an independent main effect of orientation with the illusion stronger when the face was upright. This confirmed that object knowledge was important in generating the illusion, a conclusion which was confirmed by comparison with a "hollow potato" illusion. There was an effect of light on the inverted face suggesting that the direction of light may generally affect the interpretation of surfaces as convex or concave. It was also argued that there appears to be a general preference for convex interpretations of patterns of shading. The illusion was also found to be stronger when viewed monocularly and this effect was also independent of orientation. This was consistent with the processing of shape information by independent modules with object knowledge acting as a further constraint on the final interpretation. Effects of lighting were next reported on the recognition of shaded representations of facial surfaces, with top lighting facilitating processing. The adverse effects of bottom lighting on the interpretation of facial shape appear to affect within category as well as between category discriminations. Photographic negation was also found to affect recognition performance and it was suggested that its effects may be complimentary to those of bottom lighting in some respects. These effects were reported to be dependent on view. The last set of experiments investigated the effects of lighting and view on a simultaneous face matching task using the same surface representations which required subjects to decide if two images were of the same or different people. Subjects were found to be as much affected by a change in lighting as a change in view, which seems inconsistent with edge-based accounts. Top lighting was also found to facilitate matches across changes in view. When the stimuli were inverted matches across changes in both view and light were poorer, although image differences were the same. In other experiments subjects were found to match better across changes between two directions of top lighting than between directions of bottom lighting, although the extent of the changes were the same, suggesting the importance of top lighting for lighting as well as view invariance. Inverting the stimuli, which also inverts the lighting relative to the observer, disrupted matching across directions of top lighting but facilitated matching between levels of bottom lighting, consistent with the use of shading information. Changes in size were not found to affect matching showing that the effect of lighting was not only because it changes image properties. The effect of lighting was also found to transfer to digitised photographs showing that it was not an artifact of the materials. Lastly effects of lighting were reported when images were presented sequentially showing that the effect was not an artifact of simultaneous presentation. In the final section the effects reported were considered within the framework of theories of object recognition and argued to be inconsistent with invariant features, edge-based or alignment approaches. An alternative scheme employing surface-based primitives derived from shape-from-shuding was developed to account for the pattern of effects and contrasted with an image-based accoun

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

Shape from Shading through Shape Evolution

In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark

Video-based online face recognition using identity surfaces

Recognising faces across multiple views is more challenging than that from a fixed view because of the severe non-linearity caused by rotation in depth, self-occlusion, self-shading, and change of illumination. The problem can be related to the problem of modelling the spatiotemporal dynamics of moving faces from video input for unconstrained live face recognition. Both problems remain largely under-developed. To address the problems, a novel approach is presented in this paper. A multi-view dynamic face model is designed to extract the shape-and-pose-free texture patterns of faces. The model provides a precise correspondence to the task of recognition since the 3D shape information is used to warp the multi-view faces onto the model mean shape in frontal-view. The identity surface of each subject is constructed in a discriminant feature space from a sparse set of face texture patterns, or more practically, from one or more learning sequences containing the face of the subject. Instead of matching templates or estimating multi-modal density functions, face recognition can be performed by computing the pattern distances to the identity surfaces or trajectory distances between the object and model trajectories. Experimental results depict that this approach provides an accurate recognition rate while using trajectory distances achieves a more robust performance since the trajectories encode the spatio-temporal information and contain accumulated evidence about the moving faces in a video input

Learning to Reconstruct Texture-less Deformable Surfaces from a Single View

Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured. By contrast, recovering the 3D shape of texture-less surfaces remains an open problem, and essentially relates to Shape-from-Shading. In this paper, we introduce a data-driven approach to this problem. We introduce a general framework that can predict diverse 3D representations, such as meshes, normals, and depth maps. Our experiments show that meshes are ill-suited to handle texture-less 3D reconstruction in our context. Furthermore, we demonstrate that our approach generalizes well to unseen objects, and that it yields higher-quality reconstructions than a state-of-the-art SfS technique, particularly in terms of normal estimates. Our reconstructions accurately model the fine details of the surfaces, such as the creases of a T-Shirt worn by a person.Comment: Accepted to 3DV 201

Photometric Depth Super-Resolution

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2019. First three authors contribute equall

New constraints on data-closeness and needle map consistency for shape-from-shading

This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. First, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. This not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. Second, we exploit the improved ease of control of the new shape-from-shading process to investigate various types of needle-map consistency constraint. The first set of constraints are based on needle-map smoothness. The second avenue of investigation is to use curvature information to impose topographic constraints. Third, we explore ways in which the needle-map is recovered so as to be consistent with the image gradient field. In each case we explore a variety of robust error measures and consistency weighting schemes that can be used to impose the desired constraints on the recovered needle-map. We provide an experimental assessment of the new shape-from-shading framework on both real world images and synthetic images with known ground truth surface normals. The main conclusion drawn from our analysis is that the data-closeness constraint improves the efficiency of shape-from-shading and that both the topographic and gradient consistency constraints improve the fidelity of the recovered needle-map