Visual depth(distance)perception is a fundamental aspect of environmental cognition,as it allows people to judge the spatial scale of their surroundings.However,estimating the depth of classical Chinese gardens is cha...Visual depth(distance)perception is a fundamental aspect of environmental cognition,as it allows people to judge the spatial scale of their surroundings.However,estimating the depth of classical Chinese gardens is challenging,especially from static viewpoints that frame the scenery.Previous studies have examined how the internal components of the scenery frame affect depth perception.Still,the role of the frame and its peripheral information as environmental background have been largely overlooked.This study investigates how depth perception at viewpoints is influenced by viewing position displacement,frame geometry,and environmental context.The authors created nine stimulus materials in a cave virtual reality environment(three image treatments×three positions).Seventy-one participants were asked to evaluate depth perception using the magnitude estimation and adjustment methods.Their eye movement behavior was also recorded using an eye-movement instrument(SensoMotoric Instruments(SMI)eye-tracking glasses,120 Hz).The results showed that participants could perceive spatial depth differences between viewing positions even when the internal viewpoint displacement was small;frame shape did not significantly affect depth perception and gaze behavior;and peripheral visual information of the frame enhanced depth perception significantly.Moreover,the form of the environmental background,especially the position of the scenery window,strongly guided the participants'gaze.These findings suggest that ambient visual information significantly impacts environmental experience,which landscape designers should consider.展开更多
针对动态场景下视觉SLAM(Simultaneous Localization and Mapping)系统中深度学习分割网络实时性不足,以及相机非期望运动导致位姿估计偏差的问题,提出一种基于跨域掩膜分割的视觉SLAM算法.该算法采用轻量化YOLO-fastest网络结合背景减...针对动态场景下视觉SLAM(Simultaneous Localization and Mapping)系统中深度学习分割网络实时性不足,以及相机非期望运动导致位姿估计偏差的问题,提出一种基于跨域掩膜分割的视觉SLAM算法.该算法采用轻量化YOLO-fastest网络结合背景减除法实现运动物体检测,利用深度图结合深度阈值分割构建跨域掩膜分割机制,并设计相机运动几何校正策略补偿检测框坐标误差,在实现运动物体分割的同时提升处理速度.为优化特征点利用率,采用金字塔光流对动态特征点进行帧间连续跟踪与更新,同时确保仅由静态特征点参与位姿估计过程.在TUM数据集上进行系统性评估,实验结果表明,相比于ORB-SLAM3算法,该算法的绝对位姿误差平均降幅达97.1%,与使用深度学习分割网络的DynaSLAM和DS-SLAM的动态SLAM算法相比,其单帧跟踪时间大幅减少,在精度与效率之间实现了更好的平衡.展开更多
Objective: The current study aimed to assess the association between the type of anisometropia and its effects on monocular and binocular best-corrected vision acuity (BCVA), aniseikonia, and stereopsis in the absence...Objective: The current study aimed to assess the association between the type of anisometropia and its effects on monocular and binocular best-corrected vision acuity (BCVA), aniseikonia, and stereopsis in the absence of strabismus. Methods: In total, 162 individuals with anisometropia and healthy eyes and without a previous history of amblyopia therapy and eye surgery were included in the analysis. According to spherical and cylindrical components and spherical equivalent, they were divided into the spherical hyperopic anisometropia (SHA, n = 31), spherical myopic anisometropia (SMA, n = 45), astigmatic or cylindrical hyperopic anisometropia (CHA, n = 22), and astigmatic or cylindrical myopic anisometropia (CMA, n = 64) groups. Patients without anisometropia (NA, n = 188) were classified under the control group. The effects of anisometropia on monocular and binocular BCVA, aniseikonia, and stereoacuity were examined. Results: The NA group had a significantly lower LogMAR of BCVA of the right eye (RE), left eye (LE), worse eye than the SHA, SMA, CMA, and CHA groups. Moreover, the SMA group had significantly lower LogMAR of BCVA than the CHA group (p Conclusion: Worse visual levels of the RE, LE, worse eye, BCVA difference, and lower stereopsis were evidenced in each type of anisometropia defined in this study. Cylindrical hyperopic anisometropia (CHA) resulted in a statically significant worsening VA level and stereopsis than cylindrical myopic (CMA) or spherical myopic anisometropia.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52408006,52038007)。
文摘Visual depth(distance)perception is a fundamental aspect of environmental cognition,as it allows people to judge the spatial scale of their surroundings.However,estimating the depth of classical Chinese gardens is challenging,especially from static viewpoints that frame the scenery.Previous studies have examined how the internal components of the scenery frame affect depth perception.Still,the role of the frame and its peripheral information as environmental background have been largely overlooked.This study investigates how depth perception at viewpoints is influenced by viewing position displacement,frame geometry,and environmental context.The authors created nine stimulus materials in a cave virtual reality environment(three image treatments×three positions).Seventy-one participants were asked to evaluate depth perception using the magnitude estimation and adjustment methods.Their eye movement behavior was also recorded using an eye-movement instrument(SensoMotoric Instruments(SMI)eye-tracking glasses,120 Hz).The results showed that participants could perceive spatial depth differences between viewing positions even when the internal viewpoint displacement was small;frame shape did not significantly affect depth perception and gaze behavior;and peripheral visual information of the frame enhanced depth perception significantly.Moreover,the form of the environmental background,especially the position of the scenery window,strongly guided the participants'gaze.These findings suggest that ambient visual information significantly impacts environmental experience,which landscape designers should consider.
文摘针对动态场景下视觉SLAM(Simultaneous Localization and Mapping)系统中深度学习分割网络实时性不足,以及相机非期望运动导致位姿估计偏差的问题,提出一种基于跨域掩膜分割的视觉SLAM算法.该算法采用轻量化YOLO-fastest网络结合背景减除法实现运动物体检测,利用深度图结合深度阈值分割构建跨域掩膜分割机制,并设计相机运动几何校正策略补偿检测框坐标误差,在实现运动物体分割的同时提升处理速度.为优化特征点利用率,采用金字塔光流对动态特征点进行帧间连续跟踪与更新,同时确保仅由静态特征点参与位姿估计过程.在TUM数据集上进行系统性评估,实验结果表明,相比于ORB-SLAM3算法,该算法的绝对位姿误差平均降幅达97.1%,与使用深度学习分割网络的DynaSLAM和DS-SLAM的动态SLAM算法相比,其单帧跟踪时间大幅减少,在精度与效率之间实现了更好的平衡.
文摘Objective: The current study aimed to assess the association between the type of anisometropia and its effects on monocular and binocular best-corrected vision acuity (BCVA), aniseikonia, and stereopsis in the absence of strabismus. Methods: In total, 162 individuals with anisometropia and healthy eyes and without a previous history of amblyopia therapy and eye surgery were included in the analysis. According to spherical and cylindrical components and spherical equivalent, they were divided into the spherical hyperopic anisometropia (SHA, n = 31), spherical myopic anisometropia (SMA, n = 45), astigmatic or cylindrical hyperopic anisometropia (CHA, n = 22), and astigmatic or cylindrical myopic anisometropia (CMA, n = 64) groups. Patients without anisometropia (NA, n = 188) were classified under the control group. The effects of anisometropia on monocular and binocular BCVA, aniseikonia, and stereoacuity were examined. Results: The NA group had a significantly lower LogMAR of BCVA of the right eye (RE), left eye (LE), worse eye than the SHA, SMA, CMA, and CHA groups. Moreover, the SMA group had significantly lower LogMAR of BCVA than the CHA group (p Conclusion: Worse visual levels of the RE, LE, worse eye, BCVA difference, and lower stereopsis were evidenced in each type of anisometropia defined in this study. Cylindrical hyperopic anisometropia (CHA) resulted in a statically significant worsening VA level and stereopsis than cylindrical myopic (CMA) or spherical myopic anisometropia.
