Dense 3D shape acquisition of swimming human or live fish is an important research topic for sports, biological science and so on. For this purpose, active stereo sensor is usually used in the air, however it cannot be applied to the underwater environment because of refraction, strong light attenuation and severe interference of bubbles. Passive stereo is a simple solution for capturing dynamic scenes at underwater environment, however the shape with textureless surfaces or irregular reflections cannot be recovered. Recently, the stereo camera pair with a pattern projector for adding artificial textures on the objects is proposed. However, to use the system for underwater environment, several problems should be compensated, i.e., disturbance by fluctuation and bubbles. Simple solution is to use convolutional neural network for stereo to cancel the effects of bubbles and or water fluctuation. Since it is not easy to train CNN with small size of database with large variation, we develop a special bubble generation device to efficiently create real bubble database of multiple size and density. In addition, we propose a transfer learning technique for multi-scale CNN to effectively remove bubbles and projected-patterns on the object. Further, we develop a real system and actually captured live swimming human, which has not been done before. Experiments are conducted to show the effectiveness of our method compared with the state of the art techniques.
In terms of light attenuation and disturbance problem for water medium, light transport analysis has been conducted @cite_28 @cite_13 . Narasimhan al proposed a structured-light-based 3D scanning method for strong scattering and absorption media based on light transport analysis @cite_9 . For weak scattering media, Bleier and N "uchter used cross laser projector which only achieved sparse reconstruction @cite_14 . To increase density, Campos and Codina projected parallel lines with DOE to capture underwater objects with one-shot scan @cite_2 . proposed a grid pattern to capture more dense shape with one-shot scan @cite_1 . One drawback of those one-shot scanning techniques is that reconstruction tends to be unstable even if light attenuation and disturbances are not so strong because sensitivity of pattern detection is high for subtle change of projected pattern. Some research such as @cite_34 used infrared structured light or ToF sensors, but infrared attenuates rapidly in water as shown in Fig. , and is not practical.
@cite_14 @cite_28 @cite_9 @cite_1 @cite_2 @cite_34 @cite_13
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Dense 3D reconstruction of fast moving objects could contribute to various applications such as body structure analysis, accident avoidance, and so on. In this paper, we propose a technique based on a one-shot scanning method, which reconstructs 3D shapes for each frame of a high frame-rate video capturing the scenes projected by a static pattern. To avoid instability of image processing, we restrict the number of colors used in the pattern to less than two. The proposed technique comprises (1) an efficient algorithm to eliminate ambiguity of projected parallel-line patterns by using intersection points, (2) a batch reconstruction algorithm of multiple frames by using spatio-temporal constraints, and (3) an efficient detection method of color-encoded grid pattern based on de Bruijn sequence. In the experiments, the line detection algorithm worked effectively and the dense reconstruction algorithm produces accurate and robust results. We also show the improved results by using temporal constraints. Finally, the dense reconstructions of fast moving objects in a high frame-rate video are presented. 3D scanning of moving objects has many applications, for example, marker-less motion capture, analysis on fluid dynamics, object explosion and so on. One of the approach to acquire accurate shape is a projector-camera system, especially the methods that reconstructs a shape by using a single image with static pattern is suitable for capturing fast moving object. In this paper, we propose a method that uses a grid pattern consisting of sets of parallel lines. The pattern is spatially encoded by a periodic color pattern. While informations are sparse in the camera image, the proposed method extracts the dense (pixel-wise) phase informations from the sparse pattern. As the result, continuous regions in the camera images can be extracted by analyzing the phase. Since there remain one DOF for each region, we propose the linear solution to eliminate the DOF by using geometric informations of the devices, i.e. epipolar constraint. In addition, solution space is finite because projected pattern consists of parallel lines with same intervals, the linear equation can be efficiently solved by integer least square method. In this paper, the formulations for both single and multiple projectors are presented. We evaluated the accuracy of correspondences and showed the comparison with respect to the number of projectors by simulation. Finally, the dense 3D reconstruction of moving objects are presented in the experiments. Abstract. In-situ calibration of structured light scanners in underwater environments is time-consuming and complicated. This paper presents a self-calibrating line laser scanning system, which enables the creation of dense 3D models with a single fixed camera and a freely moving hand-held cross line laser projector. The proposed approach exploits geometric constraints, such as coplanarities, to recover the depth information and is applicable without any prior knowledge of the position and orientation of the laser projector. By employing an off-the-shelf underwater camera and a waterproof housing with high power line lasers an affordable 3D scanning solution can be built. In experiments the performance of the proposed technique is studied and compared with 3D reconstruction using explicit calibration. We demonstrate that the scanning system can be applied to above-the-water as well as underwater scenes. This letter reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images. Cameras onboard autonomous and remotely operated vehicles can capture high-resolution images to map the seafloor; however, underwater image formation is subject to the complex process of light propagation through the water column. The raw images retrieved are characteristically different than images taken in air due to effects, such as absorption and scattering, which cause attenuation of light at different rates for different wavelengths. While this physical process is well described theoretically, the model depends on many parameters intrinsic to the water column as well as the structure of the scene. These factors make recovery of these parameters difficult without simplifying assumptions or field calibration; hence, restoration of underwater images is a nontrivial problem. Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in deep sea environments. Using WaterGAN, we generate a large training dataset of corresponding depth, in-air color images, and realistic underwater images. These data serve as input to a two-stage network for color correction of monocular underwater images. Our proposed pipeline is validated with testing on real data collected from both a pure water test tank and from underwater surveys collected in the field. Source code, sample datasets, and pretrained models are made publicly available.