Paper Reading AI Learner

Rethinking Few-shot 3D Point Cloud Semantic Segmentation

2024-03-01 15:14:47
Zhaochong An, Guolei Sun, Yun Liu, Fayao Liu, Zongwei Wu, Dan Wang, Luc Van Gool, Serge Belongie

Abstract

This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation. The latter results from sampling only 2,048 points, limiting semantic information and deviating from the real-world practice. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built. Moreover, we propose a novel FS-PCS model. While previous methods are based on feature optimization by mainly refining support features to enhance prototypes, our method is based on correlation optimization, referred to as Correlation Optimization Segmentation (COSeg). Specifically, we compute Class-specific Multi-prototypical Correlation (CMC) for each query point, representing its correlations to category prototypes. Then, we propose the Hyper Correlation Augmentation (HCA) module to enhance CMC. Furthermore, tackling the inherent property of few-shot training to incur base susceptibility for models, we propose to learn non-parametric prototypes for the base classes during training. The learned base prototypes are used to calibrate correlations for the background class through a Base Prototypes Calibration (BPC) module. Experiments on popular datasets demonstrate the superiority of COSeg over existing methods. The code is available at: this https URL

Abstract (translated)

本文回顾了少样本3D点云语义分割(FS-PCS),重点关注了目前最先进的两个重要问题:前景泄漏和稀疏点分布。前者是由于非均匀点采样导致的,使得模型能够更容易地分割出前景和背景之间的密度差异。后者是因为只采样了2,048个点,限制了语义信息,并远离了现实世界的实践。为了应对这些问题,我们引入了一个标准的FS-PCS设置,在此基础上构建了一个新的基准。此外,我们提出了一个新颖的FS-PCS模型。虽然以前的方法是基于主要通过优化支持特征来增强原型,但我们的方法基于相关优化,被称为相关优化分割(COSeg)。具体来说,我们计算每个查询点的类特异性多原型相关(CMC),代表其与类别原型之间的相关性。然后,我们提出了Hyper Correlation Augmentation(HCA)模块来增强CMC。此外,为了解决少样本训练固有的特性,我们提出在训练过程中学习基础类别的非参数原型。通过BPC模块校准背景类的相关,学习到的基础原型被用于通过该模块进行背景类别的相关校准。在流行数据集上的实验证明,COSeg相对于现有方法具有优越性。代码可在此处下载:https://this URL

URL

https://arxiv.org/abs/2403.00592

PDF

https://arxiv.org/pdf/2403.00592.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot