Ziqi Pang (庞子奇)

I am a fourth-year CS Ph.D. student focusing on computer vision and machine learning at University of Illinois Urabana-Champaign (UIUC), where my advisor is Prof. Yu-Xiong Wang. Before that, I graduated from Peking University (PKU) with a Bachelor degree in Computer Science.

I interned at Toyota Research Institute (TRI) with Dr. Pavel Tokmakov during my Ph.D. study. Prior to joining UIUC, I interned at Carnegie Mellon University (CMU) with Prof. Martial Hebert, practiced research at Peking University (PKU) with Prof. Shiliang Zhang, and spent an exciting year at TuSimple pushing the boundaries of autonomous driving guided by Dr. Naiyan Wang.

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My research concentrates on perception in spatial (3D) and temporal (video) contexts, which is the cornerstone for embodied agents and real-world visual assistants. Although being a so-called "perception" people, my ambition interest lies in how to unify generative modeling (LLMs, diffusion models, NeRF, etc.) with perception tasks . My goal is to unlock better scaling, better interactivity with humans, and self-improvement and self-exploration of perception models by enabling them to generate a distribution.

Text-to-image diffusion models can digest additional "graph" conditions about the relationships of entities, supporting more nuanced generation for recommendation systems, virtual arts, etc.

Simply bounding the size of memory banks improves VOS on challenging state-changes and long videos, indicating the importance of selecting relevant information from long contexts.

Frozen transformers from language models, though trained solely on textual data, can effectively improves diverse visual tasks by directly encoding visual tokens.

MV-Map is the first offboard auto-labeling pipeline for HD-Maps, whose crust is to fuse BEV perception results guided by geometric cues from NeRFs.

"Streaming forecasting" mitigates the gap between "snapshot-based" conventional motion forecasting and the streaming real-world traffic.

PF-Track is an vision-centric 3D MOT framework that dramatically decreases ID-Switches with an end-to-end framework for autonomous driving.

SST emphasize the small object sizes and sparsity of point clouds. Its sparse transformers enlight new backbones for outdoor LiDAR-based detection.

SimpleTrack is simple-yet-effective 3D MOT system with more than 200 stars on GitHub.

LiDAR-SOT is a LiDAR-based state estimation algorithm for both the onboard usage of redundancy system and offboard usage of auto-labeling.

Huge thanks to Jon Barron for proving the template for the page.