70 citations · 117 across the 16 of their papers we have counts for
16 papers
FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views
Chaoyi Zhou, Run Wang, Feng Luo +4
Recent advances in vision foundation models have revolutionized geometry reconstruction and semantic understanding. Yet, most of the existing approaches treat these capabilities in…
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization
Cho-Ying Wu, Yiqi Zhong, Junying Wang +1
Indoor robots rely on depth to perform tasks like navigation or obstacle detection, and single-image depth estimation is widely used to assist perception. Most indoor single-image…
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Shaoheng Fang, Zuhong Liu, Mingyu Wang +3
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly…
An Extensible Framework for Open Heterogeneous Collaborative Perception
Yifan Lu, Yue Hu, Yiqi Zhong +3
Collaborative perception aims to mitigate the limitations of single-agent perception, such as occlusions, by facilitating data exchange among multiple agents. However, most current…
Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow
Sizhe Wei, Yuxi Wei, Yue Hu +4
Collaborative perception can substantially boost each agent's perception ability by facilitating communication among multiple agents. However, temporal asynchrony among agents is i…
MMVP: Motion-Matrix-based Video Prediction
Yiqi Zhong, Luming Liang, Ilya Zharkov +1
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their…
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Yiqi Zhong, Xianming Liu, Deming Zhai +2
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. Howev…
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction
Cho-Ying Wu, Yiqi Zhong, Junying Wang +1
Model generalizability to unseen datasets, concerned with in-the-wild robustness, is less studied for indoor single-image depth prediction. We leverage gradient-based meta-learning…
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Shaoheng Fang, Zi Wang, Yiqi Zhong +3
Vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the s…
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Yue Hu, Shaoheng Fang, Zixing Lei +2
Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communicat…
Aware of the History: Trajectory Forecasting with the Local Behavior Data
Yiqi Zhong, Zhenyang Ni, Siheng Chen +1
The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in traject…
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Chenxin Xu +5
In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes…
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
Yiming Li, Dekun Ma, Ziyan An +4
Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally imp…
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection
Qiangeng Xu, Yiqi Zhong, Ulrich Neumann
Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding. However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5D as they cove…
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann +3
Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent…
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Yiqi Zhong, Cho-Ying Wu, Suya You +1
In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse de…