Watermarking of three-dimensional objects by digital holography.

Three-dimensional object recognition using joint fractional Fourier transform correlators with the help of digital Fresnel holography

Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding

A real-time recognition system of three-dimensional (3D) objects by use of microlens array is presented. The proposed system consists of two subsystems: one is a projection system of a 3D object into an array of two-dimensional (2D) images that have different perspectives of the 3D object and the second is an optical processor to perform the correlation. A 3D object is illuminated by incoherent light and then is projected into two-dimensional array of elemental images by use of microlens array. Use of microlens array allows us to make the system compact. Each elemental image corresponds to a different perspective of the 3D object. The set of elemental images contain information of the 3D object. After an optical incoherent-to-coherent conversion by an optically addressed spatial light modulator, an optical processor is used to perform the correlation between the input and the reference 3D objects. All the processes can be implemented optically for real-time recognition. Experimental and numerical results of the recognition of 3D objects are presented. These results show that the proposed system can recognize the 3D object even when an input object is very similar to the reference.

We present a technique for performing three-dimensional (3D) pattern recognition by use of in-line digital holography. The complex amplitude distribution generated by a 3D object at an arbitrary plane located in the Fresnel diffraction region is recorded by phase-shifting interferometry. The digital hologram contains information about the 3D object's shape, location, and orientation. This information allows us to perform 3D pattern-recognition techniques with high discrimination and to measure 3D orientation changes. Experimental results are presented.

Light detection and ranging (LiDAR) is widely used in the automotive industry as it can provide point cloud information containing precise distances. Three-dimensional (3D) object detection based on LiDAR point clouds is significant for environment perception tasks. However, feature learning for point clouds remains challenging. This paper proposes a two-stage voxel-based LiDAR 3D object detector, referred to as density-aware and neighborhood attention (DenNet), that focuses on the neighborhood information of objects. DenNet mainly integrates two modules: voxel density-aware (VDA) and neighborhood attention (NA). VDA introduces density information of the point cloud. Here, point cloud density information was added as voxel features in the voxel-based framework to alleviate the information loss during voxelization. Additionally, to extract neighbor information, the characteristics of 3D objects were analyzed for traffic scenes. The NA mechanism was adopted, which localizes the receptive field for each query to its nearest neighboring points. DenNet yielded competitive results, as compared with state-of-the-art methods, for the KITTI and One Million Scenes (ONCE) datasets; notably, it afforded an improvement of 3.96% relative to the baseline mean average precision on the more challenging ONCE dataset.

In this paper we assume that 3D objects are known as a database containing 3D co-ordinates and gray tone level of the object points. With the help of the database, we construct a sequence of planes containing a set of 2D objects. In order to reconstruct several 3D branching objects we solve the following problems: 1) divide the set of 2D objects to subsets of similar 2D objects; 2) find the branches of similar 2D objects; and 3) connect similar 2D objects and branches. We introduce the term morphological similarity to solve the first problem. We apply morphological sequences to find the branches. We determine the "corresponding" essential points to connect similar 2D objects. We performed experiments to validate the theory, using real data obtained from ore deposit.

In this paper, performance of a joint fractional correlator for digital holography based three-dimensional (3D) object recognition has been enhanced using wavelet filter. Digital Fresnel holograms of 3D objects have been optically captured and numerically reconstructed. The correlation has been carried out by comparing the two 3D images reconstructed from corresponding digital holograms with the help of joint fractional correlator. The reconstructed objects are multiplied with appropriately scaled Mexican-hat wavelet filter in Fresnel domain to improve the performance of the fractional correlator. The performance measure parameters; discrimination ratio and peak-to-correlation energy have been computed to show that the filter enhances the correlation output.KeywordsTarget ObjectFourier DomainWavelet FilterDiscrimination RatioDigital HolographyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Holographic imaging offers a reliable and fast method to capture the complete three-dimensional (3D) information of the scene from a single perspective. We review our recently proposed single-channel optical system for generating digital Fresnel holograms of 3D real-existing objects illuminated by incoherent light. In this motionless holographic technique, light is reflected, or emitted from a 3D object, propagates through a spatial light modulator (SLM), and is recorded by a digital camera. The SLM is used as a beamsplitter of the single-channel incoherent interferometer, such that each spherical beam originated from each object point is split into two spherical beams with two different curve radii. Incoherent sum of the entire interferences between all the couples of spherical beams creates the Fresnel hologram of the observed 3D object. When this hologram is reconstructed in the computer, the 3D properties of the object are revealed.

This work covers entire pipeline of a 3D immersive system, comprising acquistion and reconstruction of 3D objects, transforming the 3D objects into popular 3D format, compression of data with MPEG-4 compliant encoders and acquisition of animation motion data. For the acquisition part we created 3D human face object with the help of depth-field camera, in particular we used Microsoft Kinect. We process the raw data given by Kinect and transform it into a mesh, and texture it with photometric data. The reconstructed objects are quiet large in size and need to be compressed for an efficient network transmission. We encoded the objects using MPEG-4 encoder, and measured the performance of scalable mesh encoding techniques. For an interactive immersive application, motion data of player must be cap-tured, which is transmitted to remote client for playing the animation on the virtual character of player. For this purpose MPEG-4 standard defines a Bone Based An-imation to acquire and compress the motion data. We acquired the motion data of a player using a novel algorithm which is compliant to bone based animation. Our proposed approach of extracting motion data is computationally efficient

Digital holograms are capable of encoding three-dimensional (3D) information of a scene or object. Uniform quantization has been used in the past to compress digital holograms with some success. We have developed nonuniform quantization techniques that operate on the complex-valued hologram data for increased performance. Our digital holograms of 3D objects are captured using phase-shift digital interferometry. The quantized holographic pixels are coded using lossless techniques to achieve compression rates in excess of 40.

We report the generation of a real-time large computer generated hologram (CGH) using the wavefront recording plane (WRP) method with the aid of a graphics processing unit (GPU). The WRP method consists of two steps: the first step calculates a complex amplitude on a WRP that is placed between a 3D object and a CGH, from a three-dimensional (3D) object. The second step obtains a CGH by calculating diffraction from the WRP to the CGH. The disadvantages of the previous WRP method include the inability to record a large three-dimensional object that exceeds the size of the CGH, and the difficulty in implementing to all the steps on a GPU. We improved the WRP method using Shifted-Fresnel diffraction to solve the former problem, and all the steps could be implemented on a GPU. We show optical reconstructions from a 1,980 × 1,080 phase only CGH generated by about 3 × 10(4) object points over 90 frames per second. In other words, the improved method obtained a large CGH with about 6 mega pixels (1,980 × 1,080 × 3) from the object points at the video rate.

Deep learning for three-dimensional (3D) object classification is a promising task in digital holography. In this paper, we propose a convolutional neural network (CNN) that performs the multi-class classification of 3D objects information retrieved using off-axis Fresnel digital holography. The performance of the proposed method is evaluated for five different sets of 3D objects using loss and accuracy curves to demonstrate the proof of the concept.KeywordsDeep learningConvolutional neural network3-D objects classificationDigital holography

Recently, a method for synthesizing a hologram of three dimensional (3D) objects from captured light field array is demonstrated. The 3D objects can be captured under incoherent light illumination using a micro lens array and their orthographic projection view images are generated from the captured elemental images. The synthesized orthographic projection view images are then multiplied by the corresponding phase functions and combined to form a digital hologram. For the first time, we analysis the performance of synthesized hologram under photon counting (low light imaging) conditions. The feasibility of this technique is experimentally verified by recording the orthographic projection images using a micro lens array and the reconstructed photon counted hologram is presented with varying photoncounting measurements.

High-quality 3D objects play a crucial role in digital twins, while synthetic data generated from these objects have become essential in deep learning-based computer vision applications. The task of collecting and labeling real defects on industrial object surfaces has many challenges and efforts, while synthetic data generation feasibly replicates huge amounts of labeled data. However, synthetic datasets lack realism in their rendered images. To overcome this issue, this paper introduces a single framework for 3D industrial object reconstruction and synthetic defect generation for digital twin smart factory applications. In detail, NeRF is applied to reconstruct our custom industrial 3D objects through videos collected by a smartphone camera. Several NeRF-based models (i.e., Instant-NGP, Nerfacto, Volinga, and Tensorf) are compared to choose the best outcome for the next step of defect generation on the 3D object surface. To be fairly evaluated, we train four models using the Nerfstudio framework with our three custom datasets of two objects. From the experiment’s results, Instant-NGP and Nerfacto achieve the best outcomes, outperforming all other methods significantly. The exported meshes of 3D objects are refined using Blender before loading into NVIDIA Omniverse Code to generate defects on the surface with the Replicator. To evaluate the object detection performance and to verify the benefits of synthetic defect data, we conducted experiments with YOLO-based models on our synthetic and real-plus-synthetic defects. From the experiment’s results, the synthetic defect data contribute to improving YOLO models’ generalization capability with the highest and lowest accuracy mAP@0.5 enhancement of 18.8 and 1.5 percent on YOLOv6n and YOLOv8s, respectively.

A method is described for reducing the large error that often occurs in the measurement of three-dimensional (3D) objects using the photometric stereo method when there is a specular component in te reflection characteristics of the object’s surface. First, the change in the luminance distribution caused by the component is estimated based on the correlation between the modeled distribution and a gray-level image of the object, created by numerical experiment. Next, the shape measurement of the hemisphere object was carried out. Nurmerical experiment of three dimensional measurement of a hemispherical object showed that the mean error of normal vector can be reduced by as much as 90%. This method is applicable to machine vision, automatic manufacturing, and other industrial processes.