20 citations · 44 across the 24 of their papers we have counts for
26 papers
Amortized Anchor Refinement for Deployable Continuous-Time 4D Gaussian Reconstruction
Jingong Chen, Qingwen Zhang, Sanghyeon Jun +3
Continuous-time 4D reconstruction remains impractical on standalone XR headsets. Per-scene optimization demands deployment-infeasible compute, and lower budgets cause collapse rath…
Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing
Yixi Cai, Rundong Li, Yuhan Xie +3
LiDAR bundle adjustment is widely utilized in mapping to construct globally consistent point cloud maps. In this paper, we propose the first fully parallel computing framework to a…
FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation
Chenhan Jiang, Yu Chen, Qingwen Zhang +4
The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse and precise camera trajector…
SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data
Qingwen Zhang, Xiaomeng Zhu, Chenhan Jiang +1
Reliable 3D dynamic perception requires models that can anticipate motion beyond predefined categories, yet progress is hindered by the scarcity of dense, high-quality motion annot…
TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation
Qingwen Zhang, Chenhan Jiang, Xiaomeng Zhu +4
Self-supervised feed-forward methods for scene flow estimation offer real-time efficiency, but their supervision from two-frame point correspondences is unreliable and often breaks…
GRVS: a Generalizable and Recurrent Approach to Monocular Dynamic View Synthesis
Thomas Tanay, Mohammed Brahimi, Michal Nazarczuk +5
Synthesizing novel views from monocular videos of dynamic scenes remains a challenging problem. Scene-specific methods that optimize 4D representations with explicit motion priors…
UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles
Siyi Li, Qingwen Zhang, Ishan Khatri +4
LiDAR scene flow is the task of estimating per-point 3D motion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV)…
DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance
Ajinkya Khoche, Qingwen Zhang, Yixi Cai +2
Accurate 3D scene flow estimation is critical for autonomous systems to navigate dynamic environments safely, but creating the necessary large-scale, manually annotated datasets re…
AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics
Yi Yang, Kei Ikemura, Qingwen Zhang +5
Recent multi-task learning studies suggest that linear scalarization, when using well-chosen fixed task weights, can achieve comparable to or even better performance than complex m…
DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method
Qingwen Zhang, Xiaomeng Zhu, Yushan Zhang +3
Previous dominant methods for scene flow estimation focus mainly on input from two consecutive frames, neglecting valuable information in the temporal domain. While recent trends s…
AGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Peizheng Li, Shuxiao Ding, You Zhou +6
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vo…
HiMo: High-Speed Objects Motion Compensation in Point Clouds
Qingwen Zhang, Ajinkya Khoche, Yi Yang +4
LiDAR point cloud is essential for autonomous vehicles, but motion distortions from dynamic objects degrade the data quality. While previous work has considered distortions caused…
MambaFlow: A Novel and Flow-guided State Space Model for Scene Flow Estimation
Jiehao Luo, Jintao Cheng, Xiaoyu Tang +3
Scene flow estimation aims to predict 3D motion from consecutive point cloud frames, which is of great interest in autonomous driving field. Existing methods face challenges such a…
SSF: Sparse Long-Range Scene Flow for Autonomous Driving
Ajinkya Khoche, Qingwen Zhang, Laura Pereira Sanchez +3
Scene flow enables an understanding of the motion characteristics of the environment in the 3D world. It gains particular significance in the long-range, where object-based percept…
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Qingwen Zhang, Yi Yang, Peizheng Li +2
Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This detailed, point-level, information can help autonomous vehicles to accurately predict and…
Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
Yi Yang, Qingwen Zhang, Kei Ikemura +2
Addressing hard cases in autonomous driving, such as anomalous road users, extreme weather conditions, and complex traffic interactions, presents significant challenges. To ensure…
BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps
Mingkai Jia, Qingwen Zhang, Bowen Yang +3
Global point clouds that correctly represent the static environment features can facilitate accurate localization and robust path planning. However, dynamic objects introduce undes…
DUFOMap: Efficient Dynamic Awareness Mapping
Daniel Duberg, Qingwen Zhang, MingKai Jia +1
The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical bench…
DeFlow: Decoder of Scene Flow Network in Autonomous Driving
Qingwen Zhang, Yi Yang, Heng Fang +2
Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving. Many networks with l…
Human-Centric Autonomous Systems With LLMs for User Command Reasoning
Yi Yang, Qingwen Zhang, Ci Li +3
The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meet…
RMP: A Random Mask Pretrain Framework for Motion Prediction
Yi Yang, Qingwen Zhang, Thomas Gilles +2
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we pro…
A Dynamic Points Removal Benchmark in Point Cloud Maps
Qingwen Zhang, Daniel Duberg, Ruoyu Geng +3
In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points co…
Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model
Yingbing Chen, Ren Xin, Jie Cheng +4
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are…
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
Qingwen Zhang, Mingkai Tang, Ruoyu Geng +3
Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context…