平面相机阵列四参考视点的深度图像绘制(Depth Image Based Rendering,DIBR)方案允许用户全方位身临其境地体验场景,可有效避免虚拟视点图像边界空洞,然而该方案引入了较为显著的伪影、背景渗透等失真.为此,提出一种深度和结构相似性(St...平面相机阵列四参考视点的深度图像绘制(Depth Image Based Rendering,DIBR)方案允许用户全方位身临其境地体验场景,可有效避免虚拟视点图像边界空洞,然而该方案引入了较为显著的伪影、背景渗透等失真.为此,提出一种深度和结构相似性(Structural Similarity, SSIM)引导的四参考视点融合算法.首先,深入分析了针对平面相机阵列的四参考视点DIBR方案中失真产生的原因;然后,利用参考视点与虚拟视点间的相对位置关系进行视野错误排除,并根据恰可察觉失真模型提取融合图像的失真掩膜;最后,利用失真区域各视点的深度信息和SSIM进行自适应视点融合,进而绘制出高质量的虚拟视点图像.实验结果表明,本文算法绘制的虚拟视点图像比标准方案在SSIM和沉浸式视频峰值信噪比方面分别提升了0.001 8和1.46 d B,比文献方法在主观视觉感知方面更接近于真实图像.展开更多
The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing.T...The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing.To address this demand,a robotic automated 3D reconstruction cell which enables to autonomously plan the robot end-camera’s trajectory is developed for image acquisition and 3D modeling of the cockpit operation scene.A continuous viewpoint path planning algorithm is proposed that incorporates both 3D reconstruction quality and robot path quality into optimization process.Smoothness metrics for viewpoint position paths and orientation paths are introduced together for the first time in 3D reconstruction.To ensure safe and effective movement,two spatial constraints,Domain of View Admissible Position(DVAP)and Domain of View Admissible Orientation(DVAO),are implemented to account for robot reachability and collision avoidance.By using diffeomorphism mapping,the orientation path is transformed into 3D,consistent with the position path.Both orientation and position paths can be optimized in a unified framework to maximize the gain of reconstruction quality and path smoothness within DVAP and DVAO.The reconstruction cell is capable of automatic data acquisition and fine scene modeling,using the generated robot C-space trajectory.Simulation and physical scene experiments have confirmed the effectiveness of the proposed method to achieve highprecision 3D reconstruction while optimizing robot motion quality.展开更多
文摘平面相机阵列四参考视点的深度图像绘制(Depth Image Based Rendering,DIBR)方案允许用户全方位身临其境地体验场景,可有效避免虚拟视点图像边界空洞,然而该方案引入了较为显著的伪影、背景渗透等失真.为此,提出一种深度和结构相似性(Structural Similarity, SSIM)引导的四参考视点融合算法.首先,深入分析了针对平面相机阵列的四参考视点DIBR方案中失真产生的原因;然后,利用参考视点与虚拟视点间的相对位置关系进行视野错误排除,并根据恰可察觉失真模型提取融合图像的失真掩膜;最后,利用失真区域各视点的深度信息和SSIM进行自适应视点融合,进而绘制出高质量的虚拟视点图像.实验结果表明,本文算法绘制的虚拟视点图像比标准方案在SSIM和沉浸式视频峰值信噪比方面分别提升了0.001 8和1.46 d B,比文献方法在主观视觉感知方面更接近于真实图像.
基金supported by the National Key Research and Development Program of China(2019YFB1707505)the National Natural Science Foundation of China(Grant No.52005436)。
文摘The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing.To address this demand,a robotic automated 3D reconstruction cell which enables to autonomously plan the robot end-camera’s trajectory is developed for image acquisition and 3D modeling of the cockpit operation scene.A continuous viewpoint path planning algorithm is proposed that incorporates both 3D reconstruction quality and robot path quality into optimization process.Smoothness metrics for viewpoint position paths and orientation paths are introduced together for the first time in 3D reconstruction.To ensure safe and effective movement,two spatial constraints,Domain of View Admissible Position(DVAP)and Domain of View Admissible Orientation(DVAO),are implemented to account for robot reachability and collision avoidance.By using diffeomorphism mapping,the orientation path is transformed into 3D,consistent with the position path.Both orientation and position paths can be optimized in a unified framework to maximize the gain of reconstruction quality and path smoothness within DVAP and DVAO.The reconstruction cell is capable of automatic data acquisition and fine scene modeling,using the generated robot C-space trajectory.Simulation and physical scene experiments have confirmed the effectiveness of the proposed method to achieve highprecision 3D reconstruction while optimizing robot motion quality.
