Multiview Photometric Stereo Using Planar Mesh Parameterization

Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. Although popular methods for MVPS can provide outstanding results, they are often complex to execute and limited to isotropic material objects. To address such limitations, we present a simple, practical approach to MVPS, which works well for isotropic as well as other object material types such as anisotropic and glossy. The proposed approach in this paper exploits the benefit of uncertainty modeling in a deep neural network for a reliable fusion of photometric stereo (PS) and multi-view stereo (MVS) network predictions. Yet, contrary to the recently proposed state-of-the-art, we introduce neural volume rendering methodology for a trustworthy fusion of MVS and PS measurements. The advantage of introducing neural volume rendering is that it helps in the reliable modeling of objects with diverse material types, where existing MVS methods, PS methods, or both may fail. Furthermore, it allows us to work on neural 3D shape representation, which has recently shown outstanding results for many geometric processing tasks. Our suggested new loss function aims to fit the zero level set of the implicit neural function using the most certain MVS and PS network predictions coupled with weighted neural volume rendering cost. The proposed approach shows state-of-the-art results when tested extensively on several benchmark datasets.

We propose a robust uncalibrated multiview photometric stereo method for high quality 3D shape reconstruction. In our method, a coarse initial 3D mesh obtained using a multiview stereo method is projected onto a 2D planar domain using a planar mesh parameterization technique. We describe methods for surface normal estimation that work in the parameterized 2D space that jointly incorporates all geometric and photometric cues from multiple viewpoints. Using an estimated surface normal map, a refined 3D mesh is then recovered by computing an optimal displacement map in the same 2D planar domain. Our method avoids the need of merging view-dependent surface normal maps that is often required in conventional methods. We conduct evaluation on various real-world objects containing surfaces with specular reflections, multiple albedos, and complex topologies in both controlled and uncontrolled settings and demonstrate that accurate 3D meshes with fine geometric details can be recovered by our method.

This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency distortion due to PS and retain the global geometry of the shape. This paper proposes an approach that can effectively utilize such complementary strengths of PS and MVS. Our key idea is to combine them suitably while considering the per-pixel uncertainty of their estimates. To this end, we estimate per-pixel surface normals and depth using an uncertainty-aware deep-PS network and deep-MVS network, respectively. Uncertainty modeling helps select reliable surface normal and depth estimates at each pixel which then act as a true representative of the dense surface geometry. At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure. For dense, detailed, and precise inference of the object's surface profile, we propose to learn the implicit neural shape representation via a multilayer perceptron (MLP). Our approach encourages the MLP to converge to a natural zero-level set surface using the confident prediction from deep-PS and deep-MVS networks, providing superior dense surface reconstruction. Extensive experiments on the DiLiGenT-MV benchmark dataset show that our method provides high-quality shape recovery with a much lower memory footprint while outperforming almost all of the existing approaches.

Highly accurate 3D volumetric reconstruction is still an open research topic where the main difficulty is usually related to merging some rough estimations with high frequency details. One of the most promising methods is the fusion between multi-view stereo and photometric stereo images. Beside the intrinsic difficulties that multi-view stereo and photometric stereo in order to work reliably, supplementary problems arise when considered together. In this work, we present a volumetric approach to the multi-view photometric stereo problem. The key point of our method is the signed distance field parameterisation and its relation to the surface normal. This is exploited in order to obtain a linear partial differential equation which is solved in a variational framework, that combines multiple images from multiple points of view in a single system. In addition, the volumetric approach is naturally implemented on an octree, which allows for fast ray-tracing that reliably alleviates occlusions and cast shadows. Our approach is evaluated on synthetic and real data-sets and achieves state-of-the-art results.

This paper presents a hybrid approach for 3D reconstruction by fusing photometric stereo and multi-view stereo. The 3D surface is obtained by capturing a set of images taken from different viewpoints under time-varying illuminations. Key factors in the reconstruction process are surface normals that are obtained from photometric stereo. The surface is initialized by integrating the normals and then refined by performing iterative deformations on the initial surface and thereby optimizing image and normal consistency in multiple views. Benefiting from the employment of the deformation approach, we are able to perform image and normal consistency optimization without using matching windows. Instead, always the complete surface is back-projected. This makes the proposed approach much simpler and more robust compared to window-based approaches, which typically require global optimization with constraints on neighboring windows. Experiments on real-world data and ground-truth data show that for diffuse midsized objects without large depth discontinuities our approach improves the accuracy of the reconstructions compared to exiting approaches.

We present a modern solution to the multi-view photometric stereo problem (MVPS). Our work suitably exploits the image formation model in a MVPS experimental setup to recover the dense 3D reconstruction of an object from images. We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Contrary to the previous multi-staged framework to MVPS, where the position, iso-depth contours, or orientation measurements are estimated independently and then fused later, our method is simple to implement and realize. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network. We render the MVPS images by considering the object's surface normals for each 3D sample point along the viewing direction rather than explicitly using the density gradient in the volume space via 3D occupancy information. We optimize the proposed neural radiance field representation for the MVPS setup efficiently using a fully connected deep network to recover the 3D geometry of an object. Extensive evaluation on the DiLiGenT-MV benchmark dataset shows that our method performs better than the approaches that perform only PS or only multi-view stereo (MVS) and provides comparable results against the state-of-the-art multistage fusion methods.

Multi-view photometric stereo is well established for the shape recovery of static objects. However, it is difficult to align motion images under varying illumination so as to perform photometric stereo reconstruction for dynamic objects. To tackle this issue, this paper presents an optical flow estimation approach which works under periodically varying illuminations, and in cooperation with photometric stereo, enables high-quality 3D reconstruction of dynamic objects. Firstly, multi-view images of the moving object are captured under periodically varying illumination by the multi-camera multi-light system. Then, the optical flow is estimated to synthesize images under different illuminations for each viewpoint. Finally, the multi-view photometric stereo technique is employed to get a high accurate 3D model for each time instant. Experimental results on motion actors demonstrate that temporal successive images under varying illuminations are effectively registered, permitting accurate photometric reconstruction for moving objects.

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique that works for general isotropic materials. Our data capture setup is simple, which consists of only a digital camera and a handheld light source. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. According to our experiments, the captured shapes are accurate to 0.3 millimeters. The captured reflectance has relative root-mean-square error (RMSE) of 9%. © 2013 IEEE.

Photometric stereo (PS) is an established technique for high-detail reconstruction of 3D geometry and appearance. To correct for surface integration errors, PS is often combined with multiview stereo (MVS). With dynamic objects, PS reconstruction also faces the problem of computing optical flow (OF) for image alignment under rapid changes in illumination. Current PS methods typically compute optical flow and MVS as independent stages, each one with its own limitations and errors introduced by early regularization. In contrast, scene flow methods estimate geometry and motion, but lack the fine detail from PS. This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction. PGSF performs PS, OF, and MVS simultaneously. It is based on two key observations: (i) while image alignment improves PS, PS allows for surfaces to be relit to improve alignment, (ii) PS provides surface gradients that render the smoothness term in MVS unnecessary, leading to truly data-driven, continuous depth estimates. This synergy is demonstrated in the quality of the resulting RGB appearance, 3D geometry, and 3D motion.

We present an algorithm that fuses Multi-view stereo (MVS) and photometric stereo to reconstruct 3D model of objects filmed by multiple cameras under varying illuminations. Firstly, we obtain the surface normal scaled by albedo for each view through photometric stereo techniques. Then, based on the scaled normal, a new correspondence matching method, namely surface-consistency metric, is proposed to acquire accurate 3D positions of pixels through triangulation. After filtering the point cloud, a Poisson surface reconstruction is applied to obtain a watertight mesh. The algorithm has been implemented based on our multi-camera and multi-light acquisition system. We validate the method by complete reconstruction of challenging real objects and show experimentally that this technique can greatly improve on previous MVS results.

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.

We present an approach to reconstruct 3D fine-scale surface models for non-Lambertian objects from multi-view multi-illumination image sets. Unlike most previous work in photometric stereo, this approach works for general lighting conditions, i.e. natural outdoor illumination. Our method begins with a raw 3D model reconstructed from available multi-view stereo techniques. Considering the sparse characteristics of surface reflectance in the view-illumination space, we first estimate the diffuse appearance of the 3D model from the multiview captured images, and then refine it using the surface appearance under varying illuminations. With the separated low rank diffuse component, we exploit the photometric cues to recover detailed surface structure. Experimental results on various real world scenes validate that the proposed method is able to handle surfaces with specular reflectance even including saturated colours, highlight and cast-shadows.

This paper proposes a parallelization method for voxel-based 3D shape reconstruction. The voxel-based technique offers accurate 3D shape reconstruction by integrating information obtained from multiple images; however, it requires huge computation time for the reconstruction. Our main approach is parallel processing by voxel space division, which divides the voxel space by planes along with the lines passing through a virtual view point and assigns the process for each divided voxel space to a processing element (PE) individually. This makes it possible to reduce the amount of inter-PE communication. In addition to this, we introduce a method to decrease the amount of computation by controlling iteration count of iterative process for each voxel. Experimental results show that our method more efficiently reduces the computation time than simple horizontal domain division method.

We describe a system for high-resolution capture of moving 3D geometry, beginning with dynamic normal maps from multiple views. The normal maps are captured using active shape-from-shading (photometric stereo), with a large lighting dome providing a series of novel spherical lighting configurations. To compensate for low-frequency deformation, we perform multi-view matching and thin-plate spline deformation on the initial surfaces obtained by integrating the normal maps. Next, the corrected meshes are merged into a single mesh using a volumetric method. The final output is a set of meshes, which were impossible to produce with previous methods. The meshes exhibit details on the order of a few millimeters, and represent the performance over human-size working volumes at a temporal resolution of 60Hz.

We propose a novel method for 3d modeling using a fusion of a laser range sensor, a camera, and a flashlight. This combination provides dense normals and surface colors that can be mapped on a 3d model, whereas conventional sensors only output point clouds of the 3d geometry. Furthermore, the fusion enables formulations to be made simply and practically. Multi-view photometric stereo is used for estimating the fine normal distribution with a basic shape measured by the laser range sensor. Our photometric stereo can easily handle near-light formulation and specularity. Detailed surfaces can be shown by applying the normal map as bump mapping to the basic shape. Robust estimation and clustering are used for estimating reflection parameters. Results demonstrate that our method can estimate highly accurate reflection parameters and provide fine surface appearances using only a small amount of data. The effectiveness of our method is shown with an application of 3d content.