Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods.

Recent years have witnessed a tremendous growth in the scale and resolution of point clouds. To facilitate the applications of point cloud in downsampling tasks (e.g., point cloud classification), several task-oriented downsampling works have been developed by training with the task-specific loss with one-hot encoded label. However, these methods still suffer from performance degradation at high downsampling scales. In this paper, we propose a general semantic-preserved downsampling framework (SPDF) for point clouds by exploiting the rich knowledge inherent in the task network. Specifically, we firstly refine the previous pipeline to generate richer semantic supervised information. Then, the semantic feature learning is subdivided into label-level and feature-level to guide the training of downsampling network, which can better limit the semantic loss during downsampling. Extensive experiments on the benchmark dataset show that SPDF outperforms state-of-the-art downsampling methods.

Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance in point cloud downstream tasks(e.g., classification and retrieval). These methods first require rendering the point cloud into 2D multi-view images. However, conventional methods only project the geometry of the point cloud, and such projections inevitably suffer from a loss of point cloud semantic information due to dimensionality reduction. We propose a semantic-aware and task-oriented differentiable feature rendering (SFR), which reduces the information loss during projection by generating rendered images with more point cloud semantic information for downstream tasks. Our SFR method can be applied as a plug-and-play module added to any multi-view-based backbone network for end-to-end training. Extensive experiments on benchmark datasets show that our SFR method reaches state-of-the-art performance and brings general improvements to point cloud classification and retrieval tasks.

The dynamic flexible job shop scheduling problem (DFJSP) is frequently encountered in the modern manufacturing industry. As the intelligent manufacturing paradigm evolves, it is urgent to design a dynamic scheduling framework for handling the uncertainty and complexity in the real-time production line control. DFJSP aims to dynamically address new job random arrival so that the desired objective could be optimized, such as the minimization of makespan. The intractability of this problem can be directly reflected by the following two points. 1) the arrival times of new jobs are unknown in advance, so the scheduling framework needs to schedule them in real-time optimization; 2) Two optimization tasks, including job operation selection and machine assignment, have to be handled, which means multiple actions must be controlled simultaneously. This paper proposes a novel end-to-end hierarchical reinforcement learning framework to cope with the large-scale DFJSP. For generality, a higher-level layer is designed to automatically divide the DFJSP into a series of sub-problems with different scales, i.e., static FJSPs, which aims to achieve global optimization. Moreover, two lower-level layers are constructed to efficiently solve the sub-problems generated from the higher-level layer. One layer's policy based on the graph neural network is trained to schedule a job operation, and another policy based on the multi-layer perceptron is trained to assign a machine to process the job operation. Especially a Markov decision process (MDP), including state, action, and reward function, is designed for each layer of the hierarchy. Numerical experiments, including offline training and online testing, are conducted on several large-scale instances with diverse production configurations. The results verify the effectiveness of the proposed framework compared with the existing dynamic scheduling methods such as well-known dispatching rules and the existing heuristics.