This paper presents a local surface feature based 3D object recognition technique that is free from any training and handles texture-less objects. Our technique is proposed based on building a strong relationship among the different regions of an object using the combination of Vibration, Energy and Frequency of points in a point cloud. The robustness of the proposed technique has been validated by comparing with top-rated training free recognition techniques on the Bologna dataset. Results show that the proposed technique has performed well and efficiently as top-rated techniques on this dataset. In real time scenario, captured scenes by an RGBD camera are cluttered with many unwanted objects and background. Most of the state-of-the-art techniques (techniques that are training free and recognize texture-less objects) have not experimented on such scenes in the literature. To observe the performance, we propose to present a 3D dataset of 10 texture-less objects (including industrial and household objects). Our experimental results demonstrate that the proposed technique has outperformed other state-of-the-art techniques on the proposed dataset. We also experiment on three very cluttered and occluded RGBD datasets (Challenge, Clutter and Willow). The poor performance of all techniques on these datasets has revealed the need for more robust techniques in the future.

本文提出了一种基于局部表面特征的3D对象识别技术，该技术无需任何培训即可处理无纹理的对象。我们的技术是基于点云中点的振动，能量和频率的组合，在对象的不同区域之间建立牢固的关系而提出的。通过与Bologna数据集上的顶级免费培训识别技术进行比较，已验证了所提出技术的鲁棒性。结果表明，所提出的技术在该数据集上作为顶级技术表现良好且有效。在实时场景中，RGBD摄像机捕获的场景杂乱地布满了许多不需要的物体和背景。多数最新技术（可自由训练且可识别无纹理物体的技术）尚未在文献中针对此类场景进行实验。为了观察性能，我们建议提供一个包含10个无纹理物体（包括工业和家用物体）的3D数据集。我们的实验结果表明，所提出的技术在所提出的数据集上已经优于其他最新技术。我们还对三个非常混乱和封闭的RGBD数据集（挑战，混乱和柳树）进行了实验。所有技术在这些数据集上的表现不佳表明，将来需要更强大的技术。我们的实验结果表明，所提出的技术在所提出的数据集上已经优于其他最新技术。我们还对三个非常混乱和封闭的RGBD数据集（挑战，混乱和柳树）进行了实验。所有技术在这些数据集上的表现不佳表明，将来需要更强大的技术。我们的实验结果表明，所提出的技术在所提出的数据集上已经优于其他最新技术。我们还对三个非常混乱和封闭的RGBD数据集（挑战，混乱和柳树）进行了实验。所有技术在这些数据集上的表现不佳表明，将来需要更强大的技术。