Few-Shot Point Cloud Classification
3개 벤치마크 · 논문 4편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
What Makes for Effective Few-shot Point Cloud Classification?
Papers
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network
In the domain of 3D object classification, a fundamental challenge lies in addressing the scarcity of labeled data, which limits the applicability of traditional data-intensive learning paradigms. This challenge is parti…
3D Object ClassificationFew-Shot LearningFew-Shot Point Cloud ClassificationPoint Cloud ClassificationWhat Makes for Effective Few-shot Point Cloud Classification?
Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods require extensive data collection and ret…
BenchmarkingClassificationFew-Shot LearningFew-Shot Point Cloud Classification+1ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud Classification
Although different approaches have been proposed for 3D point cloud-related tasks, few-shot learning (FSL) of 3D point clouds still remains under-explored. In FSL, unlike traditional supervised learning, the classes …
DescriptiveFew-Shot LearningFew-Shot Point Cloud Classificationimage-classification+2Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. S…
3D Point Cloud Classification3D Point Cloud Linear ClassificationFew-Shot 3D Point Cloud ClassificationFew-Shot Point Cloud Classification+2