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Yiqing Liu

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2 papers

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ICRA Conference 2025 Conference Paper

KFCalibNet: A KansFormer-Based Self-Calibration Network for Camera and LiDAR

Zejing Xu
Yiqing Liu
Ruipeng Gao
Dan Tao
Peng Qi 0006
Ning Zhao
Zhe Fu

In autonomous driving and robotic navigation, multi-sensor fusion technology has become increasingly mainstream, with precise sensor calibration as its foundation. Traditional calibration methods rely on manual effort or specific targets, limiting adaptability to complex environments. Learning-based calibration methods still face challenges, such as insufficient overlap between the fields of view (FoV) of multiple sensors and suboptimal cross-modal feature association, which hinder accurate parameter regression. Unlike traditional CNN-based networks, we propose a KansFormer-based self-Calibration Network for camera and LiDAR (KFCalibNet) that replaces fixed activation functions and linear transformations with learnable nonlinear activation functions. This enables the extraction of more fine-grained features from both image and point cloud, significantly enhancing the network's robustness in scenarios with limited FoV overlap. We also employ a multihead attention (MHA) module to compute correlations between image and point cloud features, significantly enhancing cross-modal feature association. To reduce learning complexity, we designed KansFormer with FastKAN as the feedforward network, enabling deep fusion and regression of fine-grained cross-modal features for accurate extrinsic calibration. KFCalibNet achieves an absolute average calibration error of 0. 0965 cm in translation and 0. 0234° in rotation on the KITTI Odometry dataset, outperforming existing state-of-the-art calibration methods. Moreover, its accuracy and generalization capability have been validated across multiple real-world railway lines.

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NeurIPS Conference 2023 Conference Paper

TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration

Xinrui Chen
Yizhi Wang
Renao Yan
Yiqing Liu
Tian Guan
Yonghong He

Quantization is an effective way to compress neural networks. By reducing the bit width of the parameters, the processing efficiency of neural network models at edge devices can be notably improved. Most conventional quantization methods utilize real datasets to optimize quantization parameters and fine-tune. Due to the inevitable privacy and security issues of real samples, the existing real-data-driven methods are no longer applicable. Thus, a natural method is to introduce synthetic samples for zero-shot quantization (ZSQ). However, the conventional synthetic samples fail to retain the detailed texture feature distributions, which severely limits the knowledge transfer and performance of the quantized model. In this paper, a novel ZSQ method, TexQ is proposed to address this issue. We first synthesize a calibration image and extract its calibration center for each class with a texture feature energy distribution calibration method. Then, the calibration centers are used to guide the generator to synthesize samples. Finally, we introduce the mixup knowledge distillation module to diversify synthetic samples for fine-tuning. Extensive experiments on CIFAR10/100 and ImageNet show that TexQ is observed to perform state-of-the-art in ultra-low bit width quantization. For example, when ResNet-18 is quantized to 3-bit, TexQ achieves a 12. 18% top-1 accuracy increase on ImageNet compared to state-of-the-art methods. Code at https: //github. com/dangsingrue/TexQ.

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