Deep Learning Based Photometric Stereo from Many Images and Under Unknown Illumination

Shape from X is an interesting area of research in computer vision community. This topic is divided into passive and active methods. Example of passive methods is shape from texture, shape from defocus and shape from the silhouette. For active methods, the important categories are shape from shading and photometric stereo. In shape from shading, the cue for shape reconstruction is shading which is the relation between intensity and shape. In this case, only one image is considered. In photometric stereo, where multiple vantage points exist, 3D reconstruction considers multiple images (at least three). Photometric stereo on its own can be categorised depending on existing information of illumination directions, illumination intensities, Lambertian surfaces or non-Lambertian surfaces. This paper presents a method employing deep learning for photometric stereo where lighting and surface conditions are unknown. The proposed method is applied to a public dataset. Based on the experimental results, this method outperforms currently existing techniques

DeepShadow: Neural Shape from Shadow

This paper presents DeepShadow, a one-shot method for recovering the depth map and surface normals from photometric stereo shadow maps. Previous works that try to recover the surface normals from photometric stereo images treat cast shadows as a disturbance. We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals. We demonstrate that 3D reconstruction from shadows can even outperform shape-from-shading in certain cases. To the best of our knowledge, our method is the first to reconstruct 3D shape-from-shadows using neural networks. The method does not require any pre-training or expensive labeled data, and is optimized during inference time

Analysis of surface parametrizations for modern photometric stereo modeling

Tridimensional shape recovery based on Photometric Stereo (PS) recently received a strong improvement due to new mathematical models based on partial differential irradiance equation ratios. This modern approach to PS faces more realistic physical effects among which light attenuation and radial light propagation from a point light source. Since the approximation of the surface is performed with single step method, accurate reconstruction is prevented by sensitiveness to noise. In this paper we analyse a well-known parametrization of the tridimensional surface extending it on any auxiliary convex projection functions. Experiments on synthetic data show preliminary results where more accurate reconstruction can be achieved using more suitable parametrization specially in case of noisy input images

Photometric stereo for strong specular highlights

Photometric stereo (PS) is a fundamental technique in computer vision known to produce 3-D shape with high accuracy. The setting of PS is defined by using several input images of a static scene taken from one and the same camera position but under varying illumination. The vast majority of studies in this 3-D reconstruction method assume orthographic projection for the camera model. In addition, they mainly consider the Lambertian reflectance model as the way that light scatters at surfaces. So, providing reliable PS results from real world objects still remains a challenging task. We address 3-D reconstruction by PS using a more realistic set of assumptions combining for the first time the complete Blinn-Phong reflectance model and perspective projection. To this end, we will compare two different methods of incorporating the perspective projection into our model. Experiments are performed on both synthetic and real world images. Note that our real-world experiments do not benefit from laboratory conditions. The results show the high potential of our method even for complex real world applications such as medical endoscopy images which may include high amounts of specular highlights

Variational Uncalibrated Photometric Stereo under General Lighting

Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions, we propose an efficient principled variational approach to uncalibrated PS under general illumination. To this end, the Lambertian reflectance model is approximated through a spherical harmonic expansion, which preserves the spatial invariance of the lighting. The joint recovery of shape, reflectance and illumination is then formulated as a single variational problem. There the shape estimation is carried out directly in terms of the underlying perspective depth map, thus implicitly ensuring integrability and bypassing the need for a subsequent normal integration. To tackle the resulting nonconvex problem numerically, we undertake a two-phase procedure to initialize a balloon-like perspective depth map, followed by a "lagged" block coordinate descent scheme. The experiments validate efficiency and robustness of this approach. Across a variety of evaluations, we are able to reduce the mean angular error consistently by a factor of 2-3 compared to the state-of-the-art.Comment: Haefner and Ye contributed equall