PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

• URL: http://arxiv.org/abs/2212.01558v2
• Date: Mon, 19 Jun 2023 07:27:14 GMT
• Title: PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
• Authors: Minghua Liu, Yinhao Zhu, Hong Cai, Shizhong Han, Zhan Ling, Fatih Porikli, Hao Su
• Abstract summary: Generalizable 3D part segmentation is important but challenging in vision and robotics. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm.
• Score: 56.324516906160234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm. We also utilize multi-view 3D priors and few-shot prompt tuning to boost performance significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets shows that our method enables excellent zero-shot 3D part segmentation. Our few-shot version not only outperforms existing few-shot approaches by a large margin but also achieves highly competitive results compared to the fully supervised counterpart. Furthermore, we demonstrate that our method can be directly applied to iPhone-scanned point clouds without significant domain gaps.

Related papers

• SAM-guided Graph Cut for 3D Instance Segmentation [60.75119991853605]
  This paper addresses the challenge of 3D instance segmentation by simultaneously leveraging 3D geometric and multi-view image information. We introduce a novel 3D-to-2D query framework to effectively exploit 2D segmentation models for 3D instance segmentation. Our method achieves robust segmentation performance and can generalize across different types of scenes.
  arXiv  Detail & Related papers  (2023-12-13T18:59:58Z)
• PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation [32.2861030554128]
  PartSLIP, a recent advancement, has made significant strides in zero- and few-shot 3D part segmentation. We introduce PartSLIP++, an enhanced version designed to overcome the limitations of its predecessor. We show that PartSLIP++ demonstrates better performance over PartSLIP in both low-shot 3D semantic and instance-based object part segmentation tasks.
  arXiv  Detail & Related papers  (2023-12-05T01:33:04Z)
• DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields [68.94868475824575]
  This paper introduces a novel approach capable of generating infinite, high-quality 3D-consistent 2D annotations alongside 3D point cloud segmentations. We leverage the strong semantic prior within a 3D generative model to train a semantic decoder. Once trained, the decoder efficiently generalizes across the latent space, enabling the generation of infinite data.
  arXiv  Detail & Related papers  (2023-11-18T21:58:28Z)
• 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)
• Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation [30.18333233940194]
  We address the challenging task of few-shot and zero-shot 3D point cloud semantic segmentation. Our proposed method surpasses state-of-the-art algorithms by a considerable 7.90% and 14.82% under the 2-way 1-shot setting on S3DIS and ScanNet benchmarks, respectively.
  arXiv  Detail & Related papers  (2023-05-23T17:58:05Z)
• MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation [91.6658845016214]
  We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. We render a 3D shape from multiple views, and set up a dense correspondence learning task within the contrastive learning framework. As a result, the learned 2D representations are view-invariant and geometrically consistent.
  arXiv  Detail & Related papers  (2022-08-18T00:48:15Z)
• Semi-supervised 3D shape segmentation with multilevel consistency and part substitution [21.075426681857024]
  We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training.
  arXiv  Detail & Related papers  (2022-04-19T11:48:24Z)
• Weakly Supervised Volumetric Image Segmentation with Deformed Templates [80.04326168716493]
  We propose an approach that is truly weakly-supervised in the sense that we only need to provide a sparse set of 3D point on the surface of target objects. We will show that it outperforms a more traditional approach to weak-supervision in 3D at a reduced supervision cost.
  arXiv  Detail & Related papers  (2021-06-07T22:09:34Z)

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.