Duoduo CLIP: Efficient 3D Understanding with Multi-View Images

• URL: http://arxiv.org/abs/2406.11579v2
• Date: Thu, 17 Oct 2024 21:05:16 GMT
• Title: Duoduo CLIP: Efficient 3D Understanding with Multi-View Images
• Authors: Han-Hung Lee, Yiming Zhang, Angel X. Chang,
• Abstract summary: Duoduo CLIP is a model for 3D representation learning that learns shape encodings from multi-view images instead of point-clouds. Our approach not only shows better generalization compared to existing point cloud methods, but also reduces GPU requirements and training time.
• Score: 14.572094389643173
• License:
• Abstract: We introduce Duoduo CLIP, a model for 3D representation learning that learns shape encodings from multi-view images instead of point-clouds. The choice of multi-view images allows us to leverage 2D priors from off-the-shelf CLIP models to facilitate fine-tuning with 3D data. Our approach not only shows better generalization compared to existing point cloud methods, but also reduces GPU requirements and training time. In addition, the model is modified with cross-view attention to leverage information across multiple frames of the object which further boosts performance. Notably, our model is permutation invariant to the order of multi-view images while being pose-free. Compared to the current SOTA point cloud method that requires 480 A100 hours to train 1 billion model parameters we only require 57 A5000 hours and 87 million parameters. Multi-view images also provide more flexibility including being able to encode objects with a variable number of images, and performance scales when more views are used. In contrast, point cloud based methods require an entire scan or model of the object. We showcase this flexibility with benchmarks from images of real-world objects. Our model also achieves better performance in more fine-grained text to shape retrieval, demonstrating better text-and-shape alignment than point cloud based models.

Related papers

• Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels [69.55622471172941]
  Large-scale vision 2D vision language models, such as CLIP can be aligned with a 3D encoder to learn generalizable (open-vocabulary) 3D vision models. We propose an optimization framework: Cross-MoST: Cross-Modal Self-Training, to improve the label-free classification performance of a zero-shot 3D vision model.
  arXiv  Detail & Related papers  (2024-04-15T21:30:50Z)
• VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model [34.35449902855767]
  Two fundamental questions are what data we use for training and how to ensure multi-view consistency. We propose a dense consistent multi-view generation model that is fine-tuned from off-the-shelf video generative models. Our approach can generate 24 dense views and converges much faster in training than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-03-18T17:48:15Z)
• PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction [77.89935657608926]
  We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images. PF-LRM simultaneously estimates the relative camera poses in 1.3 seconds on a single A100 GPU.
  arXiv  Detail & Related papers  (2023-11-20T18:57:55Z)
• Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder [21.73287941143304]
  Multi-Modality Masked AutoEncoders (MAE) methods leverage both 2D images and 3D point clouds for pre-training. We introduce a novel approach employing a 3D to multi-view masked autoencoder to fully harness the multi-modal attributes of 3D point clouds. Our method outperforms state-of-the-art counterparts by a large margin in a variety of downstream tasks.
  arXiv  Detail & Related papers  (2023-11-17T22:10:03Z)
• ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion [61.37481051263816]
  Given a single image of a 3D object, this paper proposes a method (named ConsistNet) that is able to generate multiple images of the same object. Our method effectively learns 3D consistency over a frozen Zero123 backbone and can generate 16 surrounding views of the object within 40 seconds on a single A100 GPU.
  arXiv  Detail & Related papers  (2023-10-16T12:29:29Z)
• OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding [53.21204584976076]
  We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition.
  arXiv  Detail & Related papers  (2023-05-18T07:07:19Z)
• Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning? [38.06639044139636]
  This work proposes a novel Multi-view Vision-Prompt Fusion Network (MvNet) for few-shot 3D point cloud classification. MvNet achieves new state-of-the-art performance for 3D few-shot point cloud image classification.
  arXiv  Detail & Related papers  (2023-04-20T11:39:41Z)
• CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP [19.66617835750012]
  Training a 3D scene understanding model requires complicated human annotations. vision-language pre-training models (e.g., CLIP) have shown remarkable open-world reasoning properties. We propose directly transferring CLIP's feature space to 3D scene understanding model without any form of supervision.
  arXiv  Detail & Related papers  (2023-03-08T17:30:58Z)
• P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting [94.11915008006483]
  We propose a novel Point-to-Pixel prompting for point cloud analysis. Our method attains 89.3% accuracy on the hardest setting of ScanObjectNN. Our framework also exhibits very competitive performance on ModelNet classification and ShapeNet Part Code.
  arXiv  Detail & Related papers  (2022-08-04T17:59:03Z)
• Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding [80.04281842702294]
  We introduce the concept of the multi-view point cloud (Voint cloud) representing each 3D point as a set of features extracted from several view-points. This novel 3D Voint cloud representation combines the compactness of 3D point cloud representation with the natural view-awareness of multi-view representation. We deploy a Voint neural network (VointNet) with a theoretically established functional form to learn representations in the Voint space.
  arXiv  Detail & Related papers  (2021-11-30T13:08:19Z)

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.