🧭 About Me

Hello! I am Rui Qian, a Master’s student in Computer Control and Automation at Nanyang Technological University (NTU), Singapore, supervised by Prof. Lihua Xie.

My research interests lie at the intersection of 3D Computer Vision and Robotic Perception. My core academic ambition is to advance machine spatial intelligence, focusing on how autonomous agents can perceive, comprehend, and interact with our complex and dynamic 3D world in a manner akin to humans.

I am particularly interested in semantic scene understanding and developing advanced scalable 3D representation methods, such as the involving 3D Gaussian representations. I believe that true autonomy hinges on a machine’s ability to reconstruct (geometry) and understand (semantics) a persistent internal model of its environment.

In the long term, I aim to bridge geometric perception and semantic reasoning. I am passionate about developing foundation models for 3D world understanding to empower future robotic and Embodied AI systems. My goal is to enable these agents with higher-level reasoning capabilities, allowing them to build persistent, high-fidelity digital twins of their environment and ultimately, to seamlessly connect the physical and virtual worlds.

🔥 News

• 2025.11:   🎉🎉 One paper on Occupancy Networks is accepted to AAAI 2026 as Oral!
• 2025.09:   Currently seeking a PhD position, Research Assistantship, or Internship!

📝 Publications

Under Review

SplatSSC: Decoupled Depth-Guided Gaussian Splatting for 3D Semantic Scene Completion

AAAI (Oral) 2026

Rui Qian^*, Haozhi Cao^*, Tianchen Deng, Shenghai Yuan, Lihua Xie

We introduce SplatSSC, a decoupled depth-guided Gaussian splatting framework that leverages depth-aware initialization and geometry–semantic disentangled aggregation to achieve highly efficient, robust, and accurate 3D semantic scene completion, outperforming previous methods by over 6.3% IoU and 4.1% mIoU while greatly reducing computational cost.

arXiv GitHub Video

Under Review

Energy-Constrained Navigation for Planetary Rovers under Hybrid RTG-Solar Power

Under review for an international robotics conference

Tianxin Hu^*, Weixiang Guo^*, Ruimeng Liu, Xinhang Xu, Rui Qian, Jinyu Chen, Shenghai Yuan, Lihua Xie

We present an energy-constrained trajectory planning framework for planetary rovers that explicitly models hybrid RTG–solar power flow. The method ensures smooth and dynamically feasible trajectories while strictly satisfying instantaneous power and cumulative energy limits.

arXiv GitHub Video

✖ Close

Under Review

STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response

Under review for an international robotics conference

Shenghai Yuan^*, Weixiang Guo^*, Tianxin Hu, Yu Yang, Jinyu Chen, Rui Qian, Zhongyuan Liu, Lihua Xie

We present STARC, a see-through augmented reality framework for human-robot collaboration that fuses responder-mounted and robot-mounted LiDAR sensing. The system enables real-time visualization of occluded humans and hazards, enhancing situational awareness and safety in emergency response missions.

arXiv GitHub Video

🎖 Honors and Awards

📖 Educations

• 2020.010 - 2024.06, Changzhou University, Changzhou, China, Bachelor of Electronic Information Engineering.
• 2024.08 - 2025.10 (now), Nanyang Technological University, Singapore, Master of Computer Control and Automation.

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