43 citations · 66 across the 13 of their papers we have counts for
15 papers · 1 filter
Joint Orientation and Weight Optimization for Robust Watertight Surface Reconstruction via Dirichlet-Regularized Winding Fields
Jiaze Li, Daisheng Jin, Fei Hou +5
We propose Dirichlet Winding Reconstruction (DiWR), a robust method for reconstructing watertight surfaces from unoriented point clouds with non-uniform sampling, noise, and outlie…
UniE2F: A Unified Diffusion Framework for Event-to-Frame Reconstruction with Video Foundation Models
Gang Xu, Zhiyu Zhu, Junhui Hou
Event cameras excel at high-speed, low-power, and high-dynamic-range scene perception. However, as they fundamentally record only relative intensity changes rather than absolute in…
RoSe: Robust Self-supervised Stereo Matching under Adverse Weather Conditions
Yun Wang, Junjie Hu, Junhui Hou +3
Recent self-supervised stereo matching methods have made significant progress, but their performance significantly degrades under adverse weather conditions such as night, rain, an…
Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss
Yifan Zhang, Wei Zhang, Chuangxin He +2
Unsupervised online 3D instance segmentation is a fundamental yet challenging task, as it requires maintaining consistent object identities across LiDAR scans without relying on an…
Optimizing Multi-Modality Trackers via Significance-Regularized Tuning
Zhiwen Chen, Jinjian Wu, Zhiyu Zhu +3
This paper tackles the critical challenge of optimizing multi-modality trackers by effectively adapting pre-trained models for RGB data. Existing fine-tuning paradigms oscillate be…
CFTrack: Enhancing Lightweight Visual Tracking through Contrastive Learning and Feature Matching
Juntao Liang, Jun Hou, Weijun Zhang +1
Achieving both efficiency and strong discriminative ability in lightweight visual tracking is a challenge, especially on mobile and edge devices with limited computational resource…
Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?
Yifan Zhang, Junhui Hou
Cross-modal contrastive distillation has recently been explored for learning effective 3D representations. However, existing methods focus primarily on modality-shared features, ne…
E-Motion: Future Motion Simulation via Event Sequence Diffusion
Song Wu, Zhiyu Zhu, Junhui Hou +2
Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capt…
Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework
Hao Jing, Anhong Wang, Lijun Zhao +5
In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often…
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models
Yifan Zhang, Junhui Hou
Contrastive image-to-LiDAR knowledge transfer, commonly used for learning 3D representations with synchronized images and point clouds, often faces a self-conflict dilemma. This is…
Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking
Jiading Li, Zhiyu Zhu, Jinhui Hou +2
This paper tackles the problem of passive gaze estimation using both event and frame data. Considering the inherently different physiological structures, it is intractable to accur…
Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection
Shiwen Zhao, Wei Wang, Junhui Hou +1
This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Yifan Zhang, Junhui Hou, Siyu Ren +3
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D…
Segment Any Events via Weighted Adaptation of Pivotal Tokens
Zhiwen Chen, Zhiyu Zhu, Yifan Zhang +3
In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robu…
Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
Jinhui Hou, Zhiyu Zhu, Junhui Hou +3
This paper studies a diffusion-based framework to address the low-light image enhancement problem. To harness the capabilities of diffusion models, we delve into this intricate pro…