Search Results for author: Fan Lu

Found 18 papers, 9 papers with code

Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection

1 code implementation4 Dec 2023 Fan Lu, Kai Zhu, Kecheng Zheng, Wei Zhai, Yang Cao

Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously.

Out-of-Distribution Detection

HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration

no code implementations29 Oct 2023 Weiyi Xue, Fan Lu, Guang Chen

Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to patch manner, which significantly improves the registration performance.

Point Cloud Registration Pose Estimation

Convex Q Learning in a Stochastic Environment: Extended Version

no code implementations10 Sep 2023 Fan Lu, Sean Meyn

The main contributions firstly concern properties of the relaxation, described as a deterministic convex program: we identify conditions for a bounded solution, and a significant relationship between the solution to the new convex program, and the solution to standard Q-learning.


Urban Radiance Field Representation with Deformable Neural Mesh Primitives

no code implementations ICCV 2023 Fan Lu, Yan Xu, Guang Chen, Hongsheng Li, Kwan-Yee Lin, Changjun Jiang

To construct urban-level radiance fields efficiently, we design Deformable Neural Mesh Primitive~(DNMP), and propose to parameterize the entire scene with such primitives.

Image Generation Novel View Synthesis

NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation

1 code implementation CVPR 2023 Zehan Zheng, Danni Wu, Ruisi Lu, Fan Lu, Guang Chen, Changjun Jiang

In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios.

3D Point Cloud Interpolation Autonomous Driving

Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection

1 code implementation CVPR 2023 Fan Lu, Kai Zhu, Wei Zhai, Kecheng Zheng, Yang Cao

Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set.

Out-of-Distribution Detection

Sufficient Exploration for Convex Q-learning

no code implementations17 Oct 2022 Fan Lu, Prashant Mehta, Sean Meyn, Gergely Neu

The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne's LP or a version of logistic Q-learning, but has similar structure that reveals the need for regularization to avoid over-fitting.

OpenAI Gym Q-Learning

Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time

no code implementations14 Oct 2022 Fan Lu, Joel Mathias, Sean Meyn, Karanjit Kalsi

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure.


Modeling User Behavior with Graph Convolution for Personalized Product Search

1 code implementation12 Feb 2022 Fan Lu, Qimai Li, Bo Liu, Xiao-Ming Wu, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang

Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences.

Learning Semantic Representations Retrieval

HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

1 code implementation ICCV 2021 Fan Lu, Guang Chen, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, Rongqi Gu

Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet.

Point Cloud Registration

PointINet: Point Cloud Frame Interpolation Network

1 code implementation18 Dec 2020 Fan Lu, Guang Chen, Sanqing Qu, Zhijun Li, Yinlong Liu, Alois Knoll

Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.

3D Point Cloud Interpolation

MoNet: Motion-based Point Cloud Prediction Network

no code implementations21 Nov 2020 Fan Lu, Guang Chen, Yinlong Liu, Zhijun Li, Sanqing Qu, Tianpei Zou

3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene.

Autonomous Driving

LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

no code implementations16 Nov 2020 Sanqing Qu, Guang Chen, Dan Xu, Jinhu Dong, Fan Lu, Alois Knoll

At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.

Online Action Detection

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

1 code implementation NeurIPS 2020 Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll

To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points.

Point Cloud Registration Saliency Prediction

Zap Q-Learning With Nonlinear Function Approximation

no code implementations NeurIPS 2020 Shuhang Chen, Adithya M. Devraj, Fan Lu, Ana Bušić, Sean P. Meyn

Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.

OpenAI Gym Q-Learning

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