SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation

• URL: http://arxiv.org/abs/2012.10217v2
• Date: Sun, 28 Mar 2021 12:42:25 GMT
• Title: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation
• Authors: An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou
• Abstract summary: We design a weakly supervised point cloud segmentation algorithm that only requires clicking on one point per instance to indicate its location for annotation. With over-segmentation for pre-processing, we extend these location annotations into segments as seg-level labels. We show that our seg-level supervised method (SegGroup) achieves comparable results with the fully annotated point-level supervised methods.
• Score: 88.22349093672975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene. However, such strong supervision suffers from large annotation costs, arousing the need to study efficient annotating. In this paper, we discover that the locations of instances matter for 3D scene segmentation. By fully taking the advantages of locations, we design a weakly supervised point cloud segmentation algorithm that only requires clicking on one point per instance to indicate its location for annotation. With over-segmentation for pre-processing, we extend these location annotations into segments as seg-level labels. We further design a segment grouping network (SegGroup) to generate pseudo point-level labels under seg-level labels by hierarchically grouping the unlabeled segments into the relevant nearby labeled segments, so that existing point-level supervised segmentation models can directly consume these pseudo labels for training. Experimental results show that our seg-level supervised method (SegGroup) achieves comparable results with the fully annotated point-level supervised methods. Moreover, it also outperforms the recent weakly supervised methods given a fixed annotation budget.

Related papers

• Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation [42.144991202299934]
  We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations. Our core idea is to propagate the scene-level labels to each point in the point cloud by creating pseudo labels in a conservative way. We evaluate our method on ScanNet and S3DIS datasets, outperforming state of the art, and demonstrate that we can achieve results comparable to fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-11T19:18:17Z)
• ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations [29.231508413247457]
  3D instance segmentation methods often require fully-annotated dense labels for training. We present ClickSeg, a novel click-level weakly supervised 3D instance segmentation method. ClickSeg achieves $sim$90% of the accuracy of the fully-supervised counterpart.
  arXiv  Detail & Related papers  (2023-07-19T02:49:44Z)
• Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning [50.40482222266927]
  Referring Expression (RES) is aimed at localizing and segmenting the target according to the given language expression. We propose a parallel position- kernel-segmentation pipeline to better isolate and then interact with the localization and segmentation steps. Our method is simple but surprisingly effective, outperforming all previous state-of-the-art RES methods on fully- and weakly-supervised settings.
  arXiv  Detail & Related papers  (2022-12-17T08:29:33Z)
• Pointly-Supervised Panoptic Segmentation [106.68888377104886]
  We propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision. We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels and learning from them.
  arXiv  Detail & Related papers  (2022-10-25T12:03:51Z)
• Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision [63.429704654271475]
  We propose a novel weakly supervised method RWSeg that only requires labeling one object with one point. With these sparse weak labels, we introduce a unified framework with two branches to propagate semantic and instance information. Specifically, we propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages competition among different instance graphs.
  arXiv  Detail & Related papers  (2022-08-10T02:14:39Z)
• A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction [115.9169213834476]
  This paper offers a comprehensive review on label-efficient segmentation methods. We first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels. Next, we summarize the existing label-efficient segmentation methods from a unified perspective.
  arXiv  Detail & Related papers  (2022-07-04T06:21:01Z)
• Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds [59.63231842439687]
  We train a semantic point cloud segmentation network with only a small portion of points being labeled. We propose a cross-sample feature reallocating module to transfer similar features and therefore re-route the gradients across two samples. Our weakly supervised method with only 10% and 1% of labels can produce compatible results with the fully supervised counterpart.
  arXiv  Detail & Related papers  (2021-07-23T14:34:57Z)
• SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network [21.818744369503197]
  We propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net. We train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points. Our method can achieve better performance than the current semi-supervised segmentation method with fewer annotated 3D points.
  arXiv  Detail & Related papers  (2021-04-16T02:37:27Z)

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.