Jae-Woo Kim

Yirum Kim, Jaewoo Kim, Ue-Hwan Kim

Current 3D scene graph generation (3DSGG) approaches heavily rely on a single-agent assumption and small-scale environments, exhibiting limited scalability to real-world scenarios. In this work, we introduce Multi-Agent 3D Scene Graph Generation (MA3DSG) model, the first framework designed to tackle this scalability challenge using multiple agents. We develop a training-free graph alignment algorithm that efficiently merges partial query graphs from individual agents into a unified global scene graph. Leveraging extensive analysis and empirical insights, our approach enables conventional single-agent systems to operate collaboratively without requiring any learnable parameters. To rigorously evaluate 3DSGG performance, we propose MA3DSG-Bench-a benchmark that supports diverse agent configurations, domain sizes, and environmental conditions-providing a more general and extensible evaluation framework. This work lays a solid foundation for scalable, multi-agent 3DSGG research.

Doris Jung-Lin Lee, Jaewoo Kim, Renxuan Wang et al.

Scatterplots are one of the simplest and most commonly-used visualizations for understanding quantitative, multidimensional data. However, since scatterplots only depict two attributes at a time, analysts often need to manually generate and inspect large numbers of scatterplots to make sense of large datasets with many attributes. We present a visual query system for scatterplots, SCATTERSEARCH, that enables users to visually search and browse through large collections of scatterplots. Users can query for other visualizations based on a region of interest or find other scatterplots that "look similar'' to a selected one. We present two demo scenarios, provide a system overview of SCATTERSEARCH, and outline future directions.

Vaibhav Saini, Hitesh Sajnani, Jaewoo Kim et al.

Given the availability of large source-code repositories, there has been a large number of applications for large-scale clone detection. Unfortunately, despite a decade of active research, there is a marked lack in clone detectors that scale to big software systems or large repositories, specifically for detecting near-miss (Type 3) clones where significant editing activities may take place in the cloned code. This paper demonstrates: (i) SourcererCC, a token-based clone detector that targets the first three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. It uses an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone, and (ii) SourcererCC-I, an Eclipse plug-in, that uses SourcererCC's core engine to identify and navigate clones (both inter and intra project) in real-time during software development. In our experiments, comparing SourcererCC with the state-of-the-art tools, we found that it is the only clone detection tool to successfully scale to 250 MLOC on a standard workstation with 12 GB RAM and efficiently detect the first three types of clones (precision 86% and recall 86-100%). Link to the demo: https://youtu.be/l7F_9Qp-ks4