MAST: Towards Efficient Analytical Query Processing on Point Cloud Data

Jiangneng Li, Haitao Yuan, Gao Cong, Han Mao Kiah, Shuhao Zhang

Abstract:

The proliferation of 3D scanning technology, particularly within autonomous driving, has led to an exponential
increase in the volume of Point Cloud (PC) data. Given the rich semantic information contained in PC data, deep
learning models are commonly employed for tasks such as object queries. However, current query systems that
support PC data types do not process queries on semantic information. Consequently, there is a notable gap
in research regarding the efficiency of invoking deep models for each PC data query, especially when dealing
with large-scale models and datasets. To address this issue, this work aims to design an efficient approximate
approach for supporting PC analysis queries, including PC retrieval and aggregate queries. In particular, we
propose a novel framework that delivers approximate query results efficiently by sampling core PC frames
within a constrained budget, thereby minimizing the reliance on deep learning models. This framework is
underpinned by rigorous theoretical analysis, providing error-bound guarantees for the approximate results if
the sampling policy is preferred. To achieve this, we incorporate a multi-agent reinforcement learning-based
approach to optimize the sampling procedure, along with an innovative reward design leveraging spatiotemporal PC analysis. Furthermore, we exploit the spatio-temporal characteristics inherent in PC data to
construct an index that accelerates the query process. Extensive experimental evaluations demonstrate that
our proposed method, MAST, not only achieves accurate approximate query results but also maintains low
query latency, ensuring high efficiency.