# CAREER: Advancing Learning-based 3D Vision Systems for Unstructured Environment Exploration

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Clemson University (SC) · $599,945

## Abstract

Understanding and interpreting complex environments is crucial for autonomous systems to operate safely and efficiently. A self-driving vehicle must navigate through uneven terrain, a search-and-rescue drone must identify obstacles in disaster-stricken areas, and an environmental monitoring system must accurately reconstruct large-scale off-road scenes. However, existing computer vision algorithms, primarily designed for structured indoor or urban environments, often fail in these scenarios due to the unpredictable nature of off-road terrain, dynamic environmental conditions, and the scarcity of reliable visual features. This project will develop a 3D computer vision framework that fuses multiple sensing modalities, including RGB cameras, depth sensors, LiDAR, and event cameras, to enhance feature extraction, tracking, and large-scale scene reconstruction, thereby improving perception accuracy and adaptability in unstructured environments. The research will provide a foundation for next-generation autonomous perception systems, enabling significant advancements in autonomous navigation, environmental monitoring, and search-and-rescue operations. Additionally, the project will provide valuable educational opportunities by engaging students in hands-on research and promoting interdisciplinary learning in STEM.

This project will introduce a framework for learning robust 3D visual representations in unstructured environments by integrating multi-modal sensing, feature extracti

## Key facts

- **NSF award ID:** 2442792
- **Awardee organization:** Clemson University (SC)
- **SAM.gov UEI:** H2BMNX7DSKU8
- **PI:** Nianyi Li
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CAREER-Faculty Erly Career Dev, ROBUST INTELLIGENCE, EXP PROG TO STIM COMP RES
- **Estimated total:** $599,945
- **Funds obligated:** $599,945
- **Transaction type:** Standard Grant
- **Period:** 06/15/2025 → 05/31/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2442792

## Citation

> US National Science Foundation, Award 2442792, CAREER: Advancing Learning-based 3D Vision Systems for Unstructured Environment Exploration. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2442792. Licensed CC0.

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