Masked Generative Extractor for Synergistic Representation and 3D Generation of Point Clouds
- URL: http://arxiv.org/abs/2406.17342v1
- Date: Tue, 25 Jun 2024 07:57:03 GMT
- Title: Masked Generative Extractor for Synergistic Representation and 3D Generation of Point Clouds
- Authors: Hongliang Zeng, Ping Zhang, Fang Li, Jiahua Wang, Tingyu Ye, Pengteng Guo,
- Abstract summary: Masked Generative (MAGE) has demonstrated the synergistic potential between generative modeling and representation learning.
We propose Point-MAGE to extend this concept to point cloud data.
In shape classification tasks, Point-MAGE achieved an accuracy of 94.2% on the ModelNet40 dataset and 92.9% on the ScanObjectNN dataset.
- Score: 6.69660410213287
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the field of 2D image generation modeling and representation learning, Masked Generative Encoder (MAGE) has demonstrated the synergistic potential between generative modeling and representation learning. Inspired by this, we propose Point-MAGE to extend this concept to point cloud data. Specifically, this framework first utilizes a Vector Quantized Variational Autoencoder (VQVAE) to reconstruct a neural field representation of 3D shapes, thereby learning discrete semantic features of point patches. Subsequently, by combining the masking model with variable masking ratios, we achieve synchronous training for both generation and representation learning. Furthermore, our framework seamlessly integrates with existing point cloud self-supervised learning (SSL) models, thereby enhancing their performance. We extensively evaluate the representation learning and generation capabilities of Point-MAGE. In shape classification tasks, Point-MAGE achieved an accuracy of 94.2% on the ModelNet40 dataset and 92.9% (+1.3%) on the ScanObjectNN dataset. Additionally, it achieved new state-of-the-art performance in few-shot learning and part segmentation tasks. Experimental results also confirmed that Point-MAGE can generate detailed and high-quality 3D shapes in both unconditional and conditional settings.
Related papers
- SIGMA:Sinkhorn-Guided Masked Video Modeling [69.31715194419091]
Sinkhorn-guided Masked Video Modelling ( SIGMA) is a novel video pretraining method.
We distribute features of space-time tubes evenly across a limited number of learnable clusters.
Experimental results on ten datasets validate the effectiveness of SIGMA in learning more performant, temporally-aware, and robust video representations.
arXiv Detail & Related papers (2024-07-22T08:04:09Z) - HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation [106.09886920774002]
We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network.
Our method achieves consistent improvements over the baseline trained from scratch and significantly out- performs the existing schemes.
arXiv Detail & Related papers (2024-03-18T14:18:08Z) - Dynamic 3D Point Cloud Sequences as 2D Videos [81.46246338686478]
3D point cloud sequences serve as one of the most common and practical representation modalities of real-world environments.
We propose a novel generic representation called textitStructured Point Cloud Videos (SPCVs)
SPCVs re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points.
arXiv Detail & Related papers (2024-03-02T08:18:57Z) - Leveraging Large-Scale Pretrained Vision Foundation Models for
Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task.
Our approach involves making initial predictions of 2D semantic masks using different large vision models.
To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
arXiv Detail & Related papers (2023-11-03T15:41:15Z) - Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models [97.58685709663287]
generative pre-training can boost the performance of fundamental models in 2D vision.
In 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training.
We propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
arXiv Detail & Related papers (2023-07-27T16:07:03Z) - CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and
Feature Mapping [12.679625717350113]
We present CLR-GAM, a contrastive learning-based framework with Guided Augmentation (GA) for efficient dynamic exploration strategy.
We empirically demonstrate that the proposed approach achieves state-of-the-art performance on both simulated and real-world 3D point cloud datasets.
arXiv Detail & Related papers (2023-02-28T04:38:52Z) - Flow-based GAN for 3D Point Cloud Generation from a Single Image [16.04710129379503]
We introduce a hybrid explicit-implicit generative modeling scheme, which inherits the flow-based explicit generative models for sampling point clouds with arbitrary resolutions.
We evaluate on the large-scale synthetic dataset ShapeNet, with the experimental results demonstrating the superior performance of the proposed method.
arXiv Detail & Related papers (2022-10-08T17:58:20Z) - Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud
Pre-training [56.81809311892475]
Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers.
We propose Point-M2AE, a strong Multi-scale MAE pre-training framework for hierarchical self-supervised learning of 3D point clouds.
arXiv Detail & Related papers (2022-05-28T11:22:53Z) - SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection [9.924083358178239]
We propose two variants of self-attention for contextual modeling in 3D object detection.
We first incorporate the pairwise self-attention mechanism into the current state-of-the-art BEV, voxel and point-based detectors.
Next, we propose a self-attention variant that samples a subset of the most representative features by learning deformations over randomly sampled locations.
arXiv Detail & Related papers (2021-01-07T18:30:32Z) - Discrete Point Flow Networks for Efficient Point Cloud Generation [36.03093265136374]
Generative models have proven effective at modeling 3D shapes and their statistical variations.
We introduce a latent variable model that builds on normalizing flows to generate 3D point clouds of an arbitrary size.
For single-view shape reconstruction we also obtain results on par with state-of-the-art voxel, point cloud, and mesh-based methods.
arXiv Detail & Related papers (2020-07-20T14:48:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.