3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors

• URL: http://arxiv.org/abs/2105.12885v1
• Date: Thu, 27 May 2021 00:31:37 GMT
• Title: 3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors
• Abstract summary: GIDSeg is a novel approach that can simultaneously learn segmentation from sparse annotations. GIDSeg depicts global- and individual- relation via a dynamic edge convolution network. An adversarial learning module is also designed to further enhance the conditional constraint of identity descriptors.
• Score: 7.161067294394475
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training. To alleviate manual efforts, we propose GIDSeg, a novel approach that can simultaneously learn segmentation from sparse annotations via reasoning global-regional structures and individual-vicinal properties. GIDSeg depicts global- and individual- relation via a dynamic edge convolution network coupled with a kernelized identity descriptor. The ensemble effects are obtained by endowing a fine-grained receptive field to a low-resolution voxelized map. In our GIDSeg, an adversarial learning module is also designed to further enhance the conditional constraint of identity descriptors within the joint feature distribution. Despite the apparent simplicity, our proposed approach achieves superior performance over state-of-the-art for inferencing 3D dense segmentation with only sparse annotations. Particularly, with $5\%$ annotations of raw data, GIDSeg outperforms other 3D segmentation methods.

Related papers

• TrackRef3D: Multi-View Consistent Track-then-Label for Open-World Referring Segmentation in 3D Gaussian Splatting [10.787720313658093]
  We present TrackRef3D, a fully automatic pipeline that achieves open-world referring segmentation in 3D Gaussian Splatting (3DGS) without manual annotation.<n>We propose a Trajectory-Aware Semantic Consensus Module (TSCM) which aggregates cross-view predictions via synonymous clustering and trajectory-aware voting to establish a canonical semantic identity.
  arXiv  Detail & Related papers  (2026-05-26T05:49:12Z)
• Distill, Diffuse, and Semanticize (DDS): Annotation-Free 3D Scene Understanding Based on Multi-Granularity Distillation and Graph-Diffusion-Based Segmentation [6.093743600103449]
  3D semantic scene understanding is essential for digital twins, autonomous driving, smart agriculture, and embodied perception.<n>Existing annotation-free methods often face a trade-off between semantic recognition and structural efficiency.<n>We propose a resource-efficient structure-oriented framework for region-consistent and semanticized 3D scene understanding.
  arXiv  Detail & Related papers  (2026-05-08T09:39:59Z)
• Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds [53.82500407523346]
  PointINS is an instance-oriented self-supervised framework that enriches point cloud representations through geometry-aware learning.<n>PointINS achieves on average +3.5% mAP improvement for indoor instance segmentation and +4.1% PQ gain for outdoor panoptic segmentation.
  arXiv  Detail & Related papers  (2026-03-26T08:31:06Z)
• Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting [86.15347226865826]
  We design a new end-to-end object-aware lifting approach, named Unified-Lift. We augment each Gaussian point with an additional Gaussian-level feature learned using a contrastive loss to encode instance information. We conduct experiments on three benchmarks: LERF-Masked, Replica, and Messy Rooms.
  arXiv  Detail & Related papers  (2025-03-18T08:42:23Z)
• BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis [33.53327976669034]
  We revisit 3D semantic segmentation through a more granular lens, shedding light on subtle complexities that are typically overshadowed by broader performance metrics. We introduce an innovative 3D semantic segmentation network called BFANet that incorporates detailed analysis of semantic boundary features.
  arXiv  Detail & Related papers  (2025-03-16T15:13:11Z)
• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• Bayesian Self-Training for Semi-Supervised 3D Segmentation [59.544558398992386]
  3D segmentation is a core problem in computer vision. densely labeling 3D point clouds to employ fully-supervised training remains too labor intensive and expensive. Semi-supervised training provides a more practical alternative, where only a small set of labeled data is given, accompanied by a larger unlabeled set.
  arXiv  Detail & Related papers  (2024-09-12T14:54:31Z)
• Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views [10.944692719150071]
  We propose a novel 3D brain segmentation approach using complementary 2D diffusion models. Our goal is to achieve reliable segmentation quality without requiring complete labels for each individual subject.
  arXiv  Detail & Related papers  (2024-07-17T06:14:53Z)
• Panoptic Out-of-Distribution Segmentation [11.388678390784195]
  We propose Panoptic Out-of Distribution for joint pixel-level semantic in-distribution and out-of-distribution classification with instance prediction. We make the dataset, code, and trained models publicly available at http://pods.cs.uni-freiburg.de.
  arXiv  Detail & Related papers  (2023-10-18T08:38:31Z)
• 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)
• 3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks [26.29122638813974]
  We propose a framework that can robustly learn from sparse annotation using the cross-teaching of both 3D and 2D networks. Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods.
  arXiv  Detail & Related papers  (2023-07-30T15:26:17Z)
• 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)
• One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation [78.36781565047656]
  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 results are also comparable to those of the fully supervised counterparts.
  arXiv  Detail & Related papers  (2021-04-06T02:27:25Z)
• Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation [90.87105131054419]
  We present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains.
  arXiv  Detail & Related papers  (2020-12-19T21:18:03Z)

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.