Classification reliability of 3D shapes using neural networks in case of partial and noisy models

Visuo-Tactile Recognition of Partial Point Clouds Using PointNet and Curriculum Learning: Enabling Tactile Perception from Visual Data

“…Rather than proposing a handcrafted descriptor that can capture local shapes, we utilize the established point-based architecture PointNet [10] to extract taskdriven shape descriptors. The work presented in [11] noted the gap in the research of partial point cloud recognition and explored the ability of PointNet to recognize partial and noisy point clouds. The authors found that it was vital to expose the network to partial representations during training.…”

“…Existing works tackle the problem by defining handcrafted descriptors. Paganoni et al [11] instead studied partial point cloud recognition but derived samples from the ModelNet40 dataset, which is a high-quality and low-noise CAD dataset. They also analyzed the performance of Point-Net under noise without attempting to improve it.…”

“…However, as explained in the "Problem Definition" section, noise and uncertainties, due to representation differences, are core to the issue of VTR. Furthermore, in contrast to the partial point clouds that would be generated during a tactile exploration, the work in [11] used simulated laser scans and photogrammetry from single viewpoints. On the contrary, the tactile point clouds may be composed of sparse and unconnected clusters of points collected from any given surface of the object.…”