Training-free 3D Point Cloud Classification
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Benchmarks
ModelNet40
ScanObjectNN
Most implemented
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
PointCLIP: Point Cloud Understanding by CLIP
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
Papers
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point…
3D Point Cloud ClassificationClassificationPoint Cloud ClassificationTraining-free 3D Point Cloud ClassificationViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
Though the success of CLIP-based training recipes in vision-language models, their scalability to more modalities (e.g., 3D, audio, etc.) is limited to large-scale data, which is expensive or even inapplicable for rare m…
3D ClassificationQuestion AnsweringRepresentation LearningTraining-free 3D Point Cloud Classification+2Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigon…
3D Point Cloud ClassificationAllSupervised Only 3D Point Cloud ClassificationTraining-free 3D Part Segmentation+1ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that…
3D Architecture3D Classification3D Point Cloud ClassificationClassification+5PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limited and only constrained to the classific…
3D Classification3D Object Detection3D Open-Vocabulary Instance Segmentation3D Part Segmentation+10CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud dat…
3D Point Cloud ClassificationContrastive LearningFew-Shot LearningPoint Cloud Classification+4