20 citations · 44 across the 24 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…