The rapid advancement of 3D sensing and rendering technologies has expanded the use of point clouds across various fields. To address the challenge of managing large point clouds, Point Cloud Compression (PCC) has gained significant research interest in recent years. The Moving Picture Experts Group (MPEG) has played an important role in standardizing PCC by designing frameworks such as Solid Geometry-based Point Cloud Compression (Solid G-PCC), using octree and Trisoup to compress the geometry of dynamic point clouds. This paper introduces a skip coding mode to improve the inter-frame coding by early terminating octree and Trisoup node generation based on a rate-distortion optimization consideration. Experiments show a 6.3% average improvement in geometry coding efficiency.

Large Language Model (LLM) techniques play an increasingly important role in Natural Language to SQL (NL2SQL) translation. LLMs trained by extensive corpora have strong natural language understanding and basic SQL generation abilities without additional tuning specific to NL2SQL tasks. Existing LLMs-based NL2SQL approaches try to improve the translation by enhancing the LLMs with an emphasis on user intention understanding. However, LLMs sometimes fail to generate appropriate SQL due to their lack of knowledge in organizing complex logical operator composition. A promising method is to input the LLMs with demonstrations, which include known NL2SQL translations from various databases. LLMs can learn to organize operator compositions from the input demonstrations for the given task. In this paper, we propose PURPLE (Pre-trained models Utilized to Retrieve Prompts for Logical Enhancement), which improves accuracy by retrieving demonstrations containing the requisite logical operator composition for the NL2SQL task on hand, thereby guiding LLMs to produce better SQL translation. PURPLE achieves a new state-of-the-art performance of 80.5% exact-set match accuracy and 87.8% execution match accuracy on the validation set of the popular NL2SQL benchmark Spider. PURPLE maintains high accuracy across diverse benchmarks, budgetary constraints, and various LLMs, showing robustness and cost-effectiveness.