![]() ![]() ![]() Our work is inspired by the idea of local region-based techniques for volumetric illumination, which can render more realistic images. In contrast, our work provides a simple and general method that can suitably introduce the surface details coming from PS to neural radiance field representation of the scene for favorable performance gain. Of course, the precise steps taken by such approaches can provide better results, yet they rely heavily on explicit mathematical modeling, and complex multi-staged network design which are complicated to execute. Now, several fusion strategies exist that can combine these alternate sources of information for better 3D shape reconstruction. We blend those surface normal estimates with a neural radiance field representation of the scene to recover the 3D reconstruction of the object.Įxisting state-of-the-art methods to this problem generally apply a sequence of steps (i) procure the 3D position measurement using multi-view images or 3D scanner (ii) estimate surface orientation or iso-depth contours using photometric stereo methods, andįuse the surface orientation and 3D position estimates to recover 3D geometry using appropriate mathematical optimization. For each view, multiple light sources are used to estimate the surface normal. Using MVPS images, we utilize a CNN-based PS method to estimate surface normals using multiple light sources from a fixed viewpoint. Hence, MVS and PS complementary behavior in surface reconstruction from images helps us efficiently recover object shape. Further, these measuring techniques generally provide incomplete range data with outliers that require serious efforts for refinement. ![]() The major motivation for such an approach is that the active range scanning strategies used for object’s 3D acquisition, such as structured light, 3D laser scanners, RGB-D sensors Īre either complex to calibrate or provide noisy measurements or both. Nevertheless, this paper focuses on the MVPS setup, where the subject is placed on a rotating base and for each rotation multiple images are captured using one LED light source at a time. One may also prefer to use two or more active sensors to receive the surface data estimates for fusion. Accordingly, similar fusion-based strategies gained popularity for surface estimation. In such a setup, complementary modalities are used to obtain better surface measurements, which are otherwise unavailable from an individual sensor or method. ![]() As a result, a mixed experimental setup such as multi-view photometric stereo (MVPS) is generally employed. However, when it comes to the accuracy of recovered 3D shapes for its use in scientific and engineering purposes (metrology), methods that use only MVS or PS suffer. In the past, many active and passive 3D reconstruction approaches or pipelines were proposed to solve 3D reconstruction of objects. Extensive evaluation on theĭiLiGenT-MV benchmark dataset shows that our method performs better than theĪpproaches that perform only PS or only multi-view stereo (MVS) and providesĬomparable results against the state-of-the-art multi-stage fusion methods. Network to recover the 3D geometry of an object. Representation for the MVPS setup efficiently using a fully connected deep We optimize the proposed neural radiance field The object's surface normals for each 3D sample point along the viewingĭirection rather than explicitly using the density gradient in the volume space Neural rendering of multi-view images while utilizing surface normals estimatedīy a deep photometric stereo network. Previous multi-staged framework to MVPS, where the position, iso-depthĬontours, or orientation measurements are estimated independently and thenįused later, our method is simple to implement and realize. Representation to recover the object's surface geometry. Image formation model and blend it with a multi-view neural radiance field We procure the surface orientation using a photometric stereo (PS) Our work suitably exploits the image formation model in a MVPSĮxperimental setup to recover the dense 3D reconstruction of an object from We present a modern solution to the multi-view photometric stereo problem ![]()
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