Papers
1
Total Citations
3
H-Index
1
About
Ting Ma is a researcher specializing in 3D point cloud processing and indoor scene modeling, with a particular focus on the efficient reconstruction of geometric objects from sparse, unilateral point cloud data. Their most notable contribution is the development of a slicing components-guided approach for vectorized indoor object modeling, which enables the automated extraction of clean, parametric shapes—such as furniture and architectural elements—from incomplete, single-view scans. This work, published in 2022 and garnering 3 citations, addresses a critical challenge in computer vision and graphics: how to generate high-quality, editable 3D models from real-world data with minimal manual intervention. By integrating geometric priors with data-driven segmentation, Ma’s method achieves robust performance even when point clouds are noisy or occluded, offering practical value for applications in virtual reality, interior design, and robotics. While their citation count is still growing, the novelty of their approach—bridging the gap between raw sensor data and semantically meaningful vector representations—marks them as an emerging voice in the field of 3D reconstruction. Their work is particularly relevant for researchers seeking efficient, automated pipelines for digitizing indoor environments.
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Top Papers
- 1