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DinoLink: A Token-Centric Representation Compression Framework for Bandwidth-Constrained Collaborative V2X Perception
Tianle Zhu, Haohua Que, Handong Yao +2
High-precision remote perception is often hindered by the severe bandwidth constraints of Vehicle-to-Everything (V2X) networks. We propose \textit{DinoLink}, a token-centric compre…
CABLE: Cloud-Assisted Bandwidth-efficient LMM-based Encoding for V2X Systems
Haohua Que, Zhipeng Bao, Qianyi Wu +1
Cloud-hosted large multimodal models (LMMs) can provide strong open-vocabulary perception for Vehicle-to-Everything systems, but naively transmitting full-resolution frames from ed…
MACE: Mixture-of-Experts Accelerated Coordinate Encoding for Large-Scale Scene Localization and Rendering
Mingkai Liu, Dikai Fan, Haohua Que +10
Efficient localization and high-quality rendering in large-scale scenes remain a significant challenge due to the computational cost involved. While Scene Coordinate Regression (SC…
SenseExpo: Spatial Exploration and Navigation via Scene Estimation from Expeditious Predictive Operators
Haojia Gao, Haohua Que, Mingkai Liu +9
We present \textbf{SenseExpo}, a lightweight single-robot exploration framework that integrates a compact map prediction network into a frontier-based strategy. SenseExpo addresses…