Eric Jing
Papers
1
Total Citations
2
H-Index
1
About
Eric Jing is a rising researcher in computer vision and 3D scene understanding, with a focus on bridging language and 3D data. His most notable contribution is the pioneering work "OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data" (2023), which introduces a method to retrieve 3D object instances from scenes using natural language queries—without requiring any 3D training data. This approach leverages feature similarity between language and 3D segments, enabling open-vocabulary retrieval that generalizes beyond predefined categories. Though early in its citation trajectory, this work has garnered attention for its practical impact, reducing the need for costly 3D annotations. Jing’s research addresses key challenges in zero-shot learning and multimodal alignment, offering scalable solutions for robotics, augmented reality, and autonomous systems. His work exemplifies a trend toward efficient, data-light methods in 3D vision, positioning him as a promising contributor to the field.
Research Focus
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Top Papers
- 1