3D Vision · Point Clouds · Language-Guided Learning · LLM Systems
Building robust learning systems for 3D perception and intelligent agents.
while (curious) { learn(); build(); verify(); }
I am a researcher working at the intersection of 3D vision and language-guided learning, with a focus on point cloud understanding, few-shot generalization, and agent distillation.
Current focus: using language priors, adaptive supervision, and on-policy data to improve model robustness under limited annotation and distribution shift.
LLMCore.cpp NEWRunnable C++20 experiments that turn LLM systems questions into numerical checks: RoPE, KV cache, online softmax, attention layouts, speculative decoding, and tiled GEMM.
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Recoverability-aware adaptation and gated distillation for few-shot outdoor point cloud segmentation.
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Language priors and text prototypes for semantic segmentation with pretrained 3D backbones.
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On-policy relabeling for robust search-agent distillation under rollout distribution shift.
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Local-global feature learning for large-scale indoor point cloud semantic segmentation.
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- 3D scene understanding: semantic segmentation of indoor and outdoor point clouds
- Learning with limited supervision: few-shot, open-vocabulary, and distillation-based learning
- Language-guided perception: connecting text priors with pretrained 3D representations
- Agent learning: policy distillation and on-policy data collection for search agents
- LLM systems: readable C++ experiments for inference algorithms and memory behavior
My academic background includes Changzhou University and the University of Reading.