Qiyuan Wang

Zicheng Zhang, Wei Sun, Xiongkuo Min et al.

The visual quality of point clouds has been greatly emphasized since the ever-increasing 3D vision applications are expected to provide cost-effective and high-quality experiences for users. Looking back on the development of point cloud quality assessment (PCQA) methods, the visual quality is usually evaluated by utilizing single-modal information, i.e., either extracted from the 2D projections or 3D point cloud. The 2D projections contain rich texture and semantic information but are highly dependent on viewpoints, while the 3D point clouds are more sensitive to geometry distortions and invariant to viewpoints. Therefore, to leverage the advantages of both point cloud and projected image modalities, we propose a novel no-reference point cloud quality assessment (NR-PCQA) metric in a multi-modal fashion. In specific, we split the point clouds into sub-models to represent local geometry distortions such as point shift and down-sampling. Then we render the point clouds into 2D image projections for texture feature extraction. To achieve the goals, the sub-models and projected images are encoded with point-based and image-based neural networks. Finally, symmetric cross-modal attention is employed to fuse multi-modal quality-aware information. Experimental results show that our approach outperforms all compared state-of-the-art methods and is far ahead of previous NR-PCQA methods, which highlights the effectiveness of the proposed method. The code is available at https://github.com/zzc-1998/MM-PCQA.

Qiyuan Wang, Zhihui Chen, Chenye Wu

While the advanced machine learning algorithms are effective in load forecasting, they often suffer from low data utilization, and hence their superior performance relies on massive datasets. This motivates us to design machine learning algorithms with improved data utilization. Specifically, we consider the load forecasting for a new user in the system by observing only few shots (data points) of its energy consumption. This task is challenging since the limited samples are insufficient to exploit the temporal characteristics, essential for load forecasting. Nonetheless, we notice that there are not too many temporal characteristics for residential loads due to the limited kinds of human lifestyle. Hence, we propose to utilize the historical load profile data from existing users to conduct effective clustering, which mitigates the challenges brought by the limited samples. Specifically, we first design a feature extraction clustering method for categorizing historical data. Then, inheriting the prior knowledge from the clustering results, we propose a two-phase Long Short Term Memory (LSTM) model to conduct load forecasting for new users. The proposed method outperforms the traditional LSTM model, especially when the training sample size fails to cover a whole period (i.e., 24 hours in our task). Extensive case studies on two real-world datasets and one synthetic dataset verify the effectiveness and efficiency of our method.