Robust Object Classification Approach using Spherical Harmonics

Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the voxel grid of concentric spheres to learn features over the unit ball. We then limit the spherical harmonics order level to suppress the effect of noise and outliers. In addition, the entire classification operation is performed in the Fourier domain. As a result, our proposed model learned features that are less sensitive to data perturbations and corruptions. We tested our proposed model against several types of data perturbations and corruptions, such as noise and outliers. Our results show that the proposed model has fewer parameters, competes with state-of-art networks in terms of robustness to data inaccuracies, and is faster than other robust methods. Our implementation code is also publicly available1

Robust pooling through the data mode: Robust Point cloud Classification and Segmentation Through Mode Pooling

The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ability to classify or segment objects. While there are some robust deep-learning approaches, they are computationally too expensive for real-time applications. This paper proposes a deep learning solution that includes novel robust pooling layers which greatly enhance network robustness and perform significantly faster than state-of-the-art approaches. The proposed pooling layers replace conventional pooling layers in networks with global pooling operations such as PointNet and DGCNN. The proposed pooling layers look for data mode/cluster using two methods, RANSAC, and histogram, as clusters are indicative of models. We tested the proposed pooling layers on several tasks such as classification, part segmentation, and points normal vector estimation. The results show excellent robustness to high levels of data corruption with less computational requirements as compared to robust state-of-the-art methods. our code can be found at https://github.com/AymanMukh/ModePooling

Comparative Analysis of 3D Shape Recognition in the Presence of Data Inaccuracies

Classification of 3D shapes into physically meaningful categories is one of the most important tasks in understanding the immediate environment. Methods that leverage the recent advancements in deep learning have shown to outperform the traditional approaches. However, performances of those methods have only been analyzed with relatively clean data. Three-dimensional measurement sets (point clouds) produced by 3D scanners are rarely that accurate and often contain noise, outliers or missing points. This paper presents an extensive analysis of the robustness of the state-of-the-art neural network algorithms to realistic data inaccuracies. Our experiments show that the existence of these inaccuracies can significantly affect the performance of the deep learning-based algorithms

PL-Net3D: Robust 3D Object Class Recognition Using Geometric Models

Three-dimensional point clouds produced by 3D scanners are often noisy and contain outliers. Such data inaccuracies can significantly affect current deep learning-based methods and reduce their ability to classify objects. Most deep neural networks-based object classification methods were targeted to achieve high classification accuracy without considering classification robustness. Thus, despite their great success, they still fail to achieve good classification accuracy with low levels of noise and outliers. This work is carried out to develop a robust network structure that can solidly identify objects. The proposed method uses patches of planar segments, which can robustly capture object appearance. The planar segments information are then fed into a deep neural network for classification. We base our approach on the PointNet deep learning architecture. Our method was tested against several kinds of data inaccuracies such as scattered outliers, clustered outliers, noise and missing points. The proposed method shows excellent performance in the presence of these inaccuracies compared to state-of-the-art techniques. By decomposing objects into planes, the suggested method is simple, fast, provides good classification accuracy and can handle different kinds of point cloud data inaccuracies. The code can be found at https://github.com/AymanMukh/Pl-Net3D