This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts som...This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.展开更多
针对复杂室内场景中经典三维重建方法存在细节表征不足、边缘模糊、混叠伪影等问题,提出一种基于三维高斯溅射(3DGS)的复杂室内场景三维重建算法。首先提出前置点云增密网络对运动结构恢复的稀疏点云进行增密补全,构建高分辨率的增强3D...针对复杂室内场景中经典三维重建方法存在细节表征不足、边缘模糊、混叠伪影等问题,提出一种基于三维高斯溅射(3DGS)的复杂室内场景三维重建算法。首先提出前置点云增密网络对运动结构恢复的稀疏点云进行增密补全,构建高分辨率的增强3D高斯辐射场,提高对复杂场景细节部分的表征精度;随后引入场景的深度信息,设计了深度正则化损失函数,利用深度代价信息关联场景中不同深度的物体,解决结构尺度模糊问题;结合自适应密度控制策略动态优化辐射场参数,提高重建模型的精度和视觉效果。在tanks and temples和Mip-NeRF360数据集上的实验表明,该方法与传统3DGS相比,重建视图峰值信噪比分别提升了7.26%和5.03%,结构性相似和学习感知图像块相似度等指标均有改善,有效解决了三维数字图像重建中存在的精度不足和模糊混叠等问题,为室内设计、导航及数字化城市建设提供了技术支持。展开更多
文摘This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.
文摘针对复杂室内场景中经典三维重建方法存在细节表征不足、边缘模糊、混叠伪影等问题,提出一种基于三维高斯溅射(3DGS)的复杂室内场景三维重建算法。首先提出前置点云增密网络对运动结构恢复的稀疏点云进行增密补全,构建高分辨率的增强3D高斯辐射场,提高对复杂场景细节部分的表征精度;随后引入场景的深度信息,设计了深度正则化损失函数,利用深度代价信息关联场景中不同深度的物体,解决结构尺度模糊问题;结合自适应密度控制策略动态优化辐射场参数,提高重建模型的精度和视觉效果。在tanks and temples和Mip-NeRF360数据集上的实验表明,该方法与传统3DGS相比,重建视图峰值信噪比分别提升了7.26%和5.03%,结构性相似和学习感知图像块相似度等指标均有改善,有效解决了三维数字图像重建中存在的精度不足和模糊混叠等问题,为室内设计、导航及数字化城市建设提供了技术支持。
