1 citations · 2 across the 10 of their papers we have counts for
10 papers
MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting
Lilan Peng, Yandi Liu, Qingren Yao +2
Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy. Urban spatio-temporal data exhibits temporal mirage: similar short-window inp…
Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustness
Pedro Alonso, Chongshou Li, Tianrui Li
Despite extensive progress in point cloud robustness, existing methods primarily rely on augmentation strategies or defense mechanisms while overlooking the geometric nature of adv…
Late-decoupled 3D Hierarchical Semantic Segmentation with Semantic Prototype Discrimination based Bi-branch Supervision
Shuyu Cao, Chongshou Li, Jie Xu +2
3D hierarchical semantic segmentation (3DHS) is crucial for embodied intelligence applications that demand a multi-grained and multi-hierarchy understanding of 3D scenes. Despite t…
ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification
Pedro Alonso, Tianrui Li, Chongshou Li
We introduce ModelNet40-E, a new benchmark designed to assess the robustness and calibration of point cloud classification models under synthetic LiDAR-like noise. Unlike existing…
GeoCD: A Differential Local Approximation for Geodesic Chamfer Distance
Pedro Alonso, Tianrui Li, Chongshou Li
Chamfer Distance (CD) is a widely adopted metric in 3D point cloud learning due to its simplicity and efficiency. However, it suffers from a fundamental limitation: it relies solel…
LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning
Chenxi Li, Nuo Chen, Fengyun Tan +4
We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical la…
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification
Jun Chen, Xinke Li, Mingyue Xu +2
Gradient-based adversarial attacks are widely used to evaluate the robustness of 3D point cloud classifiers, yet they often rely on uniform update rules that neglect point-wise het…
Controllable 3D Outdoor Scene Generation via Scene Graphs
Yuheng Liu, Xinke Li, Yuning Zhang +6
Three-dimensional scene generation is crucial in computer vision, with applications spanning autonomous driving, gaming and the metaverse. Current methods either lack user control…
Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions
Chongshou Li, Pin Tang, Xinke Li +2
Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been relied upon. However, real-world data…
Pyramid Diffusion for Fine 3D Large Scene Generation
Yuheng Liu, Xinke Li, Xueting Li +3
Diffusion models have shown remarkable results in generating 2D images and small-scale 3D objects. However, their application to the synthesis of large-scale 3D scenes has been rar…