Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis

Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we take a step towards more discriminative 3D point cloud representation by fully taking advantages of images which inherently contain richer appearance information, e.g., texture, color, and shade. Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i.e., rendered or projected 2D images of the 3D object, to boost point cloud analysis. In practice, to effectively acquire auxiliary knowledge from view images, we develop a teacher-student framework and formulate the cross modal learning as a knowledge distillation problem. PointCMT eliminates the distribution discrepancy between different modalities through novel feature and classifier enhancement criteria and avoids potential negative transfer effectively. Note that PointCMT effectively improves the point-only representation without architecture modification. Sufficient experiments verify significant gains on various datasets using appealing backbones, i.e., equipped with PointCMT, PointNet++ and PointMLP achieve state-of-the-art performance on two benchmarks, i.e., 94.4% and 86.7% accuracy on ModelNet40 and ScanObjectNN, respectively. Code will be made available at https://github.com/ZhanHeshen/PointCMT.Comment: To appear in NIPS202

An ambipolar transistor based on a monolayer WS2 using lithium ions injection

Ambipolar field-effect transistor (FET) devices based on two-dimensional (2D) materials have been attracted much attention due to potential applications in integrated circuits, flexible electronics and optical sensors. However, it is difficult to tune Fermi level between conduction and valence bands using a traditional SiO _2 as dielectric layer. Here, we employed the lithium-ion conductive glass ceramic (LICGC) as the back-gate electrode in a monolayer WS _2 FET. The effective accumulation and dissipation of Li ^+ ions in the interface induce a wide tune of Fermi level in the conducting channel by electron and hole doping, which show an ambipolar transport characteristics with threshold voltages at 0.9 V and −1.3 V, respectively. Our results provide an opportunity for fabricating ultra-thin ambipolar FET based on 2D materials