Tactile feedback is crucial for enhancing the virtual-reality (VR) interaction experience. However, current electrotactile devices suffer from issues such as current diffusion and electrode crosstalk, limiting spatial accuracy. To address this challenge, we designed a fabric-based ultrathin flexible microelectrode array with novel stimulation–inhibition electrode units that reduces current diffusion and improves focusing, improving tactile feedback accuracy and clarity. Additionally, we developed an electrical tactile interaction evaluation system to quantitatively assess the tactile recognition accuracy and reaction time of 30 participants. Experimental results demonstrate that the proposed electrode structure and evaluation system substantially enhance tactile perception in VR environments. This system has been demonstrated through immersive scenarios such as touching running water, stroking a bird’s forehead, and feeling a cactus, highlighting its potential for providing precise tactile feedback and enhancing personalized human–computer interaction in VR.

Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth labels. However, in practice, it is not always feasible to obtain clean point clouds. In this paper, we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as ground-truth labels during training. We demonstrate that it is feasible for neural networks to only take noisy point clouds as input, and learn to approximate and restore their clean versions. In particular, we generate two noise levels for the original point clouds, requiring the second noise level to be twice the amount of the first noise level. With this, we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise, and thus learn the displacement of each noisy point in order to recover the corresponding clean point. Comprehensive experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise, obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.