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Yushan Zhang

7 accepted papers

2026

TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation

CVPR 2026

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 down under occlusions. Multi-frame supervision has the potential to provide more stable guidance by incorporating motio

Cited by 0SourcecodeScholar
2025

DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method

NeurIPS 2025spotlight

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 shift towards multi-frame reasoning, they suffer from rapidly escalating computational costs as the number of frames grow…

Cited by 0SourcecodeScholar
2024

DiffSF: Diffusion Models for Scene Flow Estimation

NeurIPS 2024spotlight

Scene flow estimation is an essential ingredient for a variety of real-world applications, especially for autonomous agents, such as self-driving cars and robots. While recent scene flow estimation approaches achieve reasonable accuracy, their applicability to real-world systems additionally benefit…

2023

GMSF: Global Matching Scene Flow

NeurIPS 2023poster

We tackle the task of scene flow estimation from point clouds. Given a source and a target point cloud, the objective is to estimate a translation from each point in the source point cloud to the target, resulting in a 3D motion vector field. Previous dominant scene flow estimation methods require c…

2023

MFA: Multi-layer Feature-aware Attack for Object Detection

UAI 2023poster

Physical adversarial attacks can mislead detectors in real-world scenarios and have attracted increasing attention. However, most existing works manipulate the detector’s final outputs as attack targets while ignoring the inherent characteristics of objects. This can result in attacks being trapped…