Shape part segmentation is a critical task in computer graphics and robotics. However, traditional supervised methods rely heavily on large amounts of labeled data, which poses significant challenges in many real-world scenarios where such data is often scarce or difficult to obtain. To address this issue, we propose an unsupervised, class-agnostic part segmentation method called Point Diffusion Segmentation (PDS). Our research demonstrates that unconditional point cloud diffusion models can capture abstract object concepts within their sub-attention layers. By extracting preliminary point cloud features from these attention maps, PDS generates efficacious segmentation results. This method fully leverages unlabeled data and proves to be highly applicable in various downstream tasks, including zero-shot part segmentation. Without resorting to any labeled data, PDS improves the zero-shot part segmentation performance of PointClipV2 by 3.1% on the ShapeNet Part dataset, setting a new state-of-the-art baseline and demonstrating significant potential of PDS.

In real-world scenes, vehicles and pedestrians on the road often exhibit consistency in their overall motion, forming the traffic flow we observe. Exploring this global collective motion consistency to aid in 3D multi-object tracking (MOT) tasks is an under-investigated issue in existing research. Recently, Graph Neural Networks (GNN) have been introduced to model interactions between targets in 3D tracking problems, achieving remarkable performance. However, existing GNN based methods usually employ neighborhood-based approaches to construct graphs which are unable to fully exploit collective relations in traffic flow. In this paper, we propose a GNN based 3D MOT method which effectively utilizes the collective motion consistency in traffic flow. Collective motion is modeled with a densely connected intra-flow graph within the collective group, allowing information to flow quickly. To build the intra-flow graph, we propose an effective collinearity condition to distinguish potential collective groups from the detected objects. For reasoning on the graph, we propose a progressive serial message-passing solver which enables the network to learn complex group movement relationships based on a thorough understanding of simple neighborhood relations. Our proposed method achieves state-of-the-art performance on public datasets: NuScenes and KITTI tracking benchmark. We have conducted extensive experiments to evaluate the comprehensive performance of our proposed method which demonstrates the effectiveness of the proposed method.

In this work, we focus on the performance benchmarking problem of storage services in cloud-native database systems, which are widely used in various cloud applications. The core idea of these systems is to separate computation and storage in traditional monolithic OLTP databases. Specifically, we first present the characteristics of two representative real I/O workloads at the storage tier of ByteDance's cloud-native database veDB. We then elaborate the limitations of using standard benchmarks such as TPC-C and YCSB to resemble these workloads. To overcome these limitations, we devise a learning-based I/O workload benchmark called CDS-Ben. We demonstrate the superiority of CDSBen by deploying it at ByteDance and showing that its generated I/O traces accurately resemble the real I/O traces in production. Additionally, we verify the accuracy and flexibility of CDSBen by generating a wide range of I/O workloads with different I/O characteristics.