Structured Light Reconstruction Research Articles

Artificial Intelligence Technology for Interactive Mobile Devices and Its Application in 3D Visual Design

The use of visual rationality in planning and designing indoor 3D spaces can effectively enhance the quality of environmental art design. For the rational planning and design of 3D spaces, it is essential to assign attribute values to the rational evaluation index of 3D space planning, determine the weight vector of rational evaluation factors for indoor 3D space planning, and accomplish the visual rationality planning and design of indoor 3D spaces. 3D reconstruction is one of the primary objectives and focal points of machine vision research. The principle is to extract 3D information from the 2D image captured by the machine’s sensors in order to reconstruct a 3D scene model. The traditional method constructs the evaluation index system for indoor 3D space planning to determine the weight of the evaluation index for alternative points. However, it overlooks the weight vector of the evaluation factors, leading to low accuracy in rational planning. This paper optimizes the effect of structured light reconstruction using photometric stereo vision. Firstly, the text delves into the impact of light sources on the 3D reconstruction effect. Subsequently, a fusion algorithm combining 3D and structured light reconstruction methods is introduced, leveraging artificial intelligence (AI) technology. Experimental results show that this algorithm has a superior 3D reconstruction effect.

Structured light reconstruction by Computer-Generated hologram in defect state

The generation of structured light from Computer-Generated Hologram (CGH) using spatial light modulators (SLM) has a wide range of applications, but there is currently a lack of research on localized defects in SLM devices. Considering that the local damage of SLM can lead to hologram defect, its result can be equivalent to the process of normal hologram generated structured beam with its near field being blocked. In this paper, CGH loaded onto digital micromirror device (DMD) is used to generate structured beams, and two cases of local defect states at the center and edge of the DMD are discussed. The results show that the reconstructed light field obtained by central defect is quite similar to the original light field, while the reconstructed light field obtained by edge defect is a much similar to the lower case of the original light field. The article further demonstrates the reconstructed results in coherent superpositions of Hermite-Gaussian modes using DMD-generated structured beams based on the single mode regime. This study expands the application scenarios for the generation of structured light beams with SLM.

Regularising disparity estimation via multi task learning with structured light reconstruction

ABSTRACT 3D reconstruction is a useful tool for surgical planning and guidance. Supervised methods for disparity/depth estimation are the state of the art, with demonstrated performances far superior to all alternatives, such as self supervised and traditional geometric methods. However, supervised training requires large datasets, and in this field, data is lacking. In this paper, we investigate the learning of structured light projections to enhance the development of disparity estimation networks. Improving supervised learning on small datasets without needing to collect extra data. We first show that it is possible to learn the projection of structured light on a scene. Secondly, we show that the joint training of structured light and disparity, using a multi-task learning (MTL) framework, improves the learning of disparity. Our MTL setup outperformed the single task learning (STL) network in every validation test. Notably, in the generalisation test, the STL error was 1.4 times worse than that of the best MTL performance. A dataset containing stereoscopic images, disparity maps and structured light projections on medical phantoms and ex vivo tissue was created for evaluation together with virtual scenes. This dataset will be made publicly available in the future.

Fast adaptive phase unwrapping algorithm based on improved bucket sorting

As a key step in active structured light reconstruction technology, the phase unwrapping algorithm has a direct impact on improving measurement accuracy and efficiency. A fast adaptive phase unwrapping algorithm based on improved bucket sorting (PUIBS) is proposed, which effectively improves the relative performance of the measurement system. First, the unreliability function of a pixel is defined according to the second-order difference, and adjacent pixels are combined into a pixel group. Then adaptively construct bucket arrays with different unreliability value ranges to meet the error transfer requirements, and put the pixel groups into bucket arrays corresponding to the unreliability value ranges in turn. Finally, according to the unreliability value of the bucket array, the phase unwrapping is performed in order from small to large. A verification experiment was carried out in a single-shot structured light system. The results show that the proposed algorithm is efficient, and successfully compared with the existing phase unwrapping algorithm. At the same time, it has a good effect on phase images with phase jump and phase occlusion.

Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images

Visual information is increasingly recognized as a useful method to detect rail surface defects due to its high efficiency and stability. However, it cannot sufficiently detect a complete defect in the complex background information. The addition of surface profiles can effectively improve this by including a 3-D information of defects. However, in high-speed detection, the traditional 3-D profile acquisition is difficult and separate from the image acquisition, which cannot satisfy the above-mentioned requirements effectively. Therefore, an unsupervised stereoscopic saliency detection method based on a binocular line-scanning system is proposed in this article. This method can simultaneously obtain a highly precise image as well as profile information while also avoids the decoding distortion of the structured light reconstruction method. In our method, a global low-rank nonnegative reconstruction algorithm with a background constraint is proposed. Unlike the low-rank recovery model, the algorithm has a more comprehensive low rank and background clustering properties. Furthermore, outlier detection based on the geometric properties of the rail surface is also proposed in this method. Finally, the image saliency results and depth outlier detection results are associated with the collaborative fusion, and a dataset (RSDDS-113) containing the rail surface defects is established for the experimental verification. The experimental results demonstrate that our method can obtain a mean absolute error of 0.09 and area under the ROC curve of 0.94, better than 15 state-of-the-art algorithms.

Texture Illumination Separation for Single-Shot Structured Light Reconstruction.

Active illumination based methods have a trade-off between acquisition time and resolution of the estimated 3D shapes. Multi-shot approaches can generate dense reconstructions but require stationary scenes. Single-shot methods are applicable to dynamic objects but can only estimate sparse reconstructions and are sensitive to surface texture. We present a single-shot approach to produce dense shape reconstructions of highly textured objects illuminated by one or more projectors. The key to our approach is an image decomposition scheme that can recover the illumination image of different projectors and the texture images of the scene from their mixed appearances. We focus on three cases of mixed appearances: the illumination from one projector onto textured surface, illumination from multiple projectors onto a textureless surface, or their combined effect. Our method can accurately compute per-pixel warps from the illumination patterns and the texture template to the observed image. The texture template is obtained by interleaving the projection sequence with an all-white pattern. The estimated warps are reliable even with infrequent interleaved projection and strong object deformation. Thus, we obtain detailed shape reconstruction and dense motion tracking of the textured surfaces. The proposed method, implemented using a one camera and two projectors system, is validated on synthetic and real data containing subtle non-rigid surface deformations.

Three-dimensional image cytometer based on widefield structured light microscopy and high-speed remote depth scanning.

A high throughput 3D image cytometer have been developed that improves imaging speed by an order of magnitude over current technologies. This imaging speed improvement was realized by combining several key components. First, a depth-resolved image can be rapidly generated using a structured light reconstruction algorithm that requires only two wide field images, one with uniform illumination and the other with structured illumination. Second, depth scanning is implemented using the high speed remote depth scanning. Finally, the large field of view, high NA objective lens and the high pixelation, high frame rate sCMOS camera enable high resolution, high sensitivity imaging of a large cell population. This system can image at 800 cell/sec in 3D at submicron resolution corresponding to imaging 1 million cells in 20 min. The statistical accuracy of this instrument is verified by quantitatively measuring rare cell populations with ratio ranging from 1:1 to 1:10(5) . © 2014 International Society for Advancement of Cytometry.

High resolution acquisition of detailed surfaces with lens-shifted structured light

We present a novel 3D geometry acquisition technique at high resolution based on structured light reconstruction with a low-cost projector–camera system. Using a 1D mechanical lens-shifter extension in the projector light path, the projected pattern is shifted in subpixel scale steps with a granularity of up to 2048 steps per projected pixel, which opens up novel possibilities in depth accuracy and smoothness for the acquired geometry. Combining the mechanical lens-shifter extension with a multiple phase shifting technique yields a measuring range of 120×80 mm while at the same time providing a high depth resolution of better than 100 μ m . Reaching beyond depth resolutions achieved by conventional structured light scanning approaches with projector–camera systems, depth layering effects inherent to conventional techniques are fully avoided. Relying on low-cost consumer products only, we reach an area resolution of down to 55 μ m (limited by the camera). We see two main benefits. First, our acquisition setup can reconstruct finest details of small cultural heritage objects such as antique coins and thus digitally preserve them in appropriate precision. Second, our accurate height fields are a viable input to physically based rendering in combination with measured material BRDFs to reproduce compelling spatially varying, material-specific effects.