7 citations · 13 across the 12 of their papers we have counts for
14 papers
Towards Spatial Supersensing in the Wild
Tianjun Gu, Tianyu Xin, Kuan Zhang +12
Humans can efficiently parse continuous sensory streams, from hours to years, scaffolding an internal world model that grounds spatial reasoning and prediction. To mimic this capac…
WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence
Xiangyu Han, Mengyu Yang, Jiaqi Li +9
Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable sca…
Thinking in 360°: Humanoid Visual Search in the Wild
Heyang Yu, Yinan Han, Xiangyu Zhang +9
Humans rely on the synergistic control of head (cephalomotor) and eye (oculomotor) to efficiently search for visual information in 360°. However, prior approaches to visual search…
Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI
Xinhao Liu, Jiaqi Li, Youming Deng +7
Reproducible closed-loop evaluation remains a major bottleneck in Embodied AI such as visual navigation. A promising path forward is high-fidelity simulation that combines photorea…
Extrapolated Urban View Synthesis Benchmark
Xiangyu Han, Zhen Jia, Boyi Li +8
Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs). At their core is Novel View Synthesis (NVS), a crucial capabili…
Adversarial Exploitation of Data Diversity Improves Visual Localization
Sihang Li, Siqi Tan, Bowen Chang +3
Visual localization, which estimates a camera's pose within a known scene, is a fundamental capability for autonomous systems. While absolute pose regression (APR) methods have sho…
NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments
Taiyi Pan, Junyang He, Chao Chen +2
Visual place recognition (VPR) enables autonomous robots to identify previously visited locations, which contributes to tasks like simultaneous localization and mapping (SLAM). VPR…
Tell Me Where You Are: Multimodal LLMs Meet Place Recognition
Zonglin Lyu, Juexiao Zhang, Mingxuan Lu +2
Large language models (LLMs) exhibit a variety of promising capabilities in robotics, including long-horizon planning and commonsense reasoning. However, their performance in place…
Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset
Yiming Li, Zhiheng Li, Nuo Chen +5
Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle research. However, these datasets are usually collected from a single vehicle's one-time pass of…
Memorize What Matters: Emergent Scene Decomposition from Multitraverse
Yiming Li, Zehong Wang, Yue Wang +5
Humans naturally retain memories of permanent elements, while ephemeral moments often slip through the cracks of memory. This selective retention is crucial for robotic perception,…
EgoPAT3Dv2: Predicting 3D Action Target from 2D Egocentric Vision for Human-Robot Interaction
Irving Fang, Yuzhong Chen, Yifan Wang +8
A robot's ability to anticipate the 3D action target location of a hand's movement from egocentric videos can greatly improve safety and efficiency in human-robot interaction (HRI)…
ActFormer: Scalable Collaborative Perception via Active Queries
Suozhi Huang, Juexiao Zhang, Yiming Li +1
Collaborative perception leverages rich visual observations from multiple robots to extend a single robot's perception ability beyond its field of view. Many prior works receive me…
Collaborative Visual Place Recognition
Yiming Li, Zonglin Lyu, Mingxuan Lu +3
Visual place recognition (VPR) capabilities enable autonomous robots to navigate complex environments by discovering the environment's topology based on visual input. Most research…
LiDAR-based 4D Occupancy Completion and Forecasting
Xinhao Liu, Moonjun Gong, Qi Fang +4
Scene completion and forecasting are two popular perception problems in research for mobile agents like autonomous vehicles. Existing approaches treat the two problems in isolation…