One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the object is too fast with respect to the exposure time of the image sensor, patterns on the captured image are blurred and reconstruction fails. In this paper, we impose multiple projection patterns into each single captured image to realize temporal super resolution of the depth image sequences. With our method, multiple patterns are projected onto the object with higher fps than possible with a camera. In this case, the observed pattern varies depending on the depth and motion of the object, so we can extract temporal information of the scene from each single image. The decoding process is realized using a learning-based approach where no geometric calibration is needed. Experiments confirm the effectiveness of our method where sequential shapes are reconstructed from a single image. Both quantitative evaluations and comparisons with recent techniques were also conducted.
There are several papers, which use multiple patterns to reconstruct a moving object @cite_2 @cite_13 , however, they capture each pattern in individual frames and do not capture multiple exposure of patterns into a single frame, it is difficult for them to achieve reconstruction of faster motion than camera fps nor temporal super-resolution.
@cite_13 @cite_2
2019110835 2131563332 124515947 2461005315
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. We present a novel 3D scanning system combining stereo and active illumination based on phase-shift for robust and accurate scene reconstruction. Stereo overcomes the traditional phase discontinuity problem and allows for the reconstruction of complex scenes containing multiple objects. Due to the sequential recording of three patterns, motion will introduce artifacts in the reconstruction. We develop a closed-form expression for the motion error in order to apply motion compensation on a pixel level. The resulting scanning system can capture accurate depth maps of complex dynamic scenes at 17 fps and can cope with both rigid and deformable objects. Single-shot structured light methods allow 3D reconstruction of dynamic scenes. However, such methods lose spatial resolution and perform poorly around depth discontinuities. Previous single-shot methods project the same pattern repeatedly; thereby spatial resolution is reduced even if the scene is static or has slowly moving parts. We present a structured light system using a sequence of shifted stripe patterns that is decodable both spatially and temporally. By default, our method allows single-shot 3D reconstruction with any of our projected patterns by using spatial windows. Moreover, the sequence is designed so as to progressively improve the reconstruction quality around depth discontinuities by using temporal windows. Our method enables motion-aware reconstruction for each pixel: The best spatio-temporal window is automatically selected depending on the scene structure, motion, and the number of available images. This significantly reduces the number of pixels around discontinuities where depth cannot be recovered in traditional approaches. Our decoding scheme extends the adaptive window matching commonly used in stereo by incorporating temporal windows with 1D spatial windows. We demonstrate the advantages of our approach for a variety of scenarios including thin structures, dynamic scenes, and scenes containing both static and dynamic regions. We contribute a new pipeline for live multi-view performance capture, generating temporally coherent high-quality reconstructions in real-time. Our algorithm supports both incremental reconstruction, improving the surface estimation over time, as well as parameterizing the nonrigid scene motion. Our approach is highly robust to both large frame-to-frame motion and topology changes, allowing us to reconstruct extremely challenging scenes. We demonstrate advantages over related real-time techniques that either deform an online generated template or continually fuse depth data nonrigidly into a single reference model. Finally, we show geometric reconstruction results on par with offline methods which require orders of magnitude more processing time and many more RGBD cameras.