SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB Images

• URL: http://arxiv.org/abs/2412.05274v1
• Date: Fri, 06 Dec 2024 18:59:04 GMT
• Title: SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB Images
• Authors: Jiahua Dong, Tong Wu, Rui Qian, Jiaqi Wang,
• Abstract summary: SimC3D is a 3D contrastive learning framework for pretraining backbones from pure RGB image data. Traditional multi-modal frameworks facilitate 3D pretraining with 2D priors by utilizing an additional 2D backbone. SimC3D directly employs 2D positional embeddings as a stronger contrastive objective, eliminating the necessity for 2D backbones.
• Score: 42.69443644770913
• License:
• Abstract: The 3D contrastive learning paradigm has demonstrated remarkable performance in downstream tasks through pretraining on point cloud data. Recent advances involve additional 2D image priors associated with 3D point clouds for further improvement. Nonetheless, these existing frameworks are constrained by the restricted range of available point cloud datasets, primarily due to the high costs of obtaining point cloud data. To this end, we propose SimC3D, a simple but effective 3D contrastive learning framework, for the first time, pretraining 3D backbones from pure RGB image data. SimC3D performs contrastive 3D pretraining with three appealing properties. (1) Pure image data: SimC3D simplifies the dependency of costly 3D point clouds and pretrains 3D backbones using solely RBG images. By employing depth estimation and suitable data processing, the monocular synthesized point cloud shows great potential for 3D pretraining. (2) Simple framework: Traditional multi-modal frameworks facilitate 3D pretraining with 2D priors by utilizing an additional 2D backbone, thereby increasing computational expense. In this paper, we empirically demonstrate that the primary benefit of the 2D modality stems from the incorporation of locality information. Inspired by this insightful observation, SimC3D directly employs 2D positional embeddings as a stronger contrastive objective, eliminating the necessity for 2D backbones and leading to considerable performance improvements. (3) Strong performance: SimC3D outperforms previous approaches that leverage ground-truth point cloud data for pretraining in various downstream tasks. Furthermore, the performance of SimC3D can be further enhanced by combining multiple image datasets, showcasing its significant potential for scalability. The code will be available at https://github.com/Dongjiahua/SimC3D.

Related papers

• Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting [7.070581940661794]
  We propose an efficient framework named GS$3$ to learn point cloud representation. Specifically, we back-project the input RGB-D images into 3D space and use a point cloud encoder to extract point-wise features.
  arXiv  Detail & Related papers  (2024-11-27T16:11:45Z)
• ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images [19.02348585677397]
  Open-vocabulary 3D object detection (OV-3Det) aims to generalize beyond the limited number of base categories labeled during the training phase. The biggest bottleneck is the scarcity of annotated 3D data, whereas 2D image datasets are abundant and richly annotated. We propose a novel framework ImOV3D to leverage pseudo multimodal representation containing both images and point clouds (PC) to close the modality gap.
  arXiv  Detail & Related papers  (2024-10-31T15:02:05Z)
• Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers [38.08724410736292]
  This paper attempts to leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. We propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds.
  arXiv  Detail & Related papers  (2024-07-18T06:32:45Z)
• 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)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• 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)
• Video Pretraining Advances 3D Deep Learning on Chest CT Tasks [63.879848037679224]
  Pretraining on large natural image classification datasets has aided model development on data-scarce 2D medical tasks. These 2D models have been surpassed by 3D models on 3D computer vision benchmarks. We show video pretraining for 3D models can enable higher performance on smaller datasets for 3D medical tasks.
  arXiv  Detail & Related papers  (2023-04-02T14:46:58Z)
• 3D Point Cloud Pre-training with Knowledge Distillation from 2D Images [128.40422211090078]
  We propose a knowledge distillation method for 3D point cloud pre-trained models to acquire knowledge directly from the 2D representation learning model. Specifically, we introduce a cross-attention mechanism to extract concept features from 3D point cloud and compare them with the semantic information from 2D images. In this scheme, the point cloud pre-trained models learn directly from rich information contained in 2D teacher models.
  arXiv  Detail & Related papers  (2022-12-17T23:21:04Z)
• Data Efficient 3D Learner via Knowledge Transferred from 2D Model [30.077342050473515]
  We deal with the data scarcity challenge of 3D tasks by transferring knowledge from strong 2D models via RGB-D images. We utilize a strong and well-trained semantic segmentation model for 2D images to augment RGB-D images with pseudo-label. Our method already outperforms existing state-of-the-art that is tailored for 3D label efficiency.
  arXiv  Detail & Related papers  (2022-03-16T09:14:44Z)

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.