GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers

• URL: http://arxiv.org/abs/2307.13251v1
• Date: Tue, 25 Jul 2023 04:43:22 GMT
• Title: GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers
• Authors: Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen
• Abstract summary: GaPro is a new instance segmentation for 3D point clouds using axis-aligned 3D bounding box supervision. Our two-step approach involves generating pseudo labels from box annotations and training a 3DIS network with the resulting labels. Our experiments show that GaPro outperforms previous weakly supervised 3D instance segmentation methods.
• Score: 14.88505076974645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer vision, where state-of-the-art methods are mainly based on full supervision. As annotating ground truth dense instance masks is tedious and expensive, solving 3DIS with weak supervision has become more practical. In this paper, we propose GaPro, a new instance segmentation for 3D point clouds using axis-aligned 3D bounding box supervision. Our two-step approach involves generating pseudo labels from box annotations and training a 3DIS network with the resulting labels. Additionally, we employ the self-training strategy to improve the performance of our method further. We devise an effective Gaussian Process to generate pseudo instance masks from the bounding boxes and resolve ambiguities when they overlap, resulting in pseudo instance masks with their uncertainty values. Our experiments show that GaPro outperforms previous weakly supervised 3D instance segmentation methods and has competitive performance compared to state-of-the-art fully supervised ones. Furthermore, we demonstrate the robustness of our approach, where we can adapt various state-of-the-art fully supervised methods to the weak supervision task by using our pseudo labels for training. The source code and trained models are available at https://github.com/VinAIResearch/GaPro.

Related papers

• Extreme Point Supervised Instance Segmentation [28.191795758445352]
  This paper introduces a novel approach to learning instance segmentation using extreme points, i.e., the topmost, leftmost, bottommost, and rightmost points, of each object. These points are readily available in the modern bounding box annotation process while offering strong clues for precise segmentation. Our model generates high-quality masks when a target object is separated into multiple parts, where previous box-supervised methods often fail.
  arXiv  Detail & Related papers  (2024-05-31T09:37:39Z)
• Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection [38.15872244768199]
  Self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA) These techniques typically select pseudo labels, i.e., 3D boxes, to supervise models for the target domain. Previous techniques mitigate this by reweighting these boxes as pseudo labels, but these boxes can still poison the training process. We propose a novel pseudo label refinery framework to improve the reliability of pseudo boxes.
  arXiv  Detail & Related papers  (2024-04-30T09:20:35Z)
• Weakly Supervised 3D Instance Segmentation without Instance-level Annotations [57.615325809883636]
  3D semantic scene understanding tasks have achieved great success with the emergence of deep learning, but often require a huge amount of manually annotated training data. We propose the first weakly-supervised 3D instance segmentation method that only requires categorical semantic labels as supervision. By generating pseudo instance labels from categorical semantic labels, our designed approach can also assist existing methods for learning 3D instance segmentation at reduced annotation cost.
  arXiv  Detail & Related papers  (2023-08-03T12:30:52Z)
• You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding [107.06117227661204]
  We propose One Thing One Click'', meaning that the annotator only needs to label one point per object. We iteratively conduct the training and label propagation, facilitated by a graph propagation module. Our model can be compatible to 3D instance segmentation equipped with a point-clustering strategy.
  arXiv  Detail & Related papers  (2023-03-26T13:57:00Z)
• Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency [78.76508318592552]
  Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. Most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. We propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images.
  arXiv  Detail & Related papers  (2023-03-15T15:14:00Z)
• Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning [59.64695628433855]
  We propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images. Basically, we design a dual-branch network equipped with an active labeling strategy, to maximize the power of tiny parts of labels. Our method even outperforms the state-of-the-art fully supervised competitors with less than 1% actively selected annotations.
  arXiv  Detail & Related papers  (2022-09-16T07:59:04Z)
• 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)
• Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes [38.60444957213202]
  We look at weakly-supervised 3D semantic instance segmentation. Key idea is to leverage 3D bounding box labels which are easier and faster to annotate. We show that it is possible to train dense segmentation models using only bounding box labels.
  arXiv  Detail & Related papers  (2022-06-02T17:59:57Z)
• 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)

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.