Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds

• URL: http://arxiv.org/abs/2212.12402v1
• Date: Fri, 23 Dec 2022 15:42:01 GMT
• Title: Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds
• Authors: Shenglan Du, Nail Ibrahimli, Jantien Stoter, Julian Kooij, Liangliang Nan
• Abstract summary: We propose a boundary-aware feature propagation mechanism to improve semantic segmentation near object boundaries. With one shared encoder, our network outputs (i) boundary localization, (ii) prediction of directions pointing to the object's interior, and (iii) semantic segmentation, in three parallel streams. Our proposed approach yields consistent improvements by reducing boundary errors.
• Score: 0.5249805590164901
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local information at low-level layers, posing significant challenges to accurate scene segmentation and precise object boundary delineation. Prior works either address this issue by post-processing or jointly learn object boundaries to implicitly improve feature encoding of the networks. These approaches often require additional modules which are difficult to integrate into the original architecture. To improve the segmentation near object boundaries, we propose a boundary-aware feature propagation mechanism. This mechanism is achieved by exploiting a multi-task learning framework that aims to explicitly guide the boundaries to their original locations. With one shared encoder, our network outputs (i) boundary localization, (ii) prediction of directions pointing to the object's interior, and (iii) semantic segmentation, in three parallel streams. The predicted boundaries and directions are fused to propagate the learned features to refine the segmentation. We conduct extensive experiments on the S3DIS and SensatUrban datasets against various baseline methods, demonstrating that our proposed approach yields consistent improvements by reducing boundary errors. Our code is available at https://github.com/shenglandu/PushBoundary.

Related papers

• PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion [25.12551124399544]
  U-shape networks cannot effectively resolve this challenge due to the lack of boundary shape constraints. We reconceptualize boundary generation by encompassing the interaction dynamics adjacent adjacent regions. Core ingredients of Net contain the pushing and pulling branches to squeeze the boundary region.
  arXiv  Detail & Related papers  (2023-12-13T17:50:31Z)
• ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction [62.599588577671796]
  We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly applicable to scenarios with real-time constraints, such as robotics or mixed reality.
  arXiv  Detail & Related papers  (2023-11-29T20:30:18Z)
• Temporal Action Localization with Enhanced Instant Discriminability [66.76095239972094]
  Temporal action detection (TAD) aims to detect all action boundaries and their corresponding categories in an untrimmed video. We propose a one-stage framework named TriDet to resolve imprecise predictions of action boundaries by existing methods. Experimental results demonstrate the robustness of TriDet and its state-of-the-art performance on multiple TAD datasets.
  arXiv  Detail & Related papers  (2023-09-11T16:17:50Z)
• Contrastive Boundary Learning for Point Cloud Segmentation [81.7289734276872]
  We propose a novel contrastive boundary learning framework for point cloud segmentation. We experimentally show that CBL consistently improves different baselines and assists them to achieve compelling performance on boundaries.
  arXiv  Detail & Related papers  (2022-03-10T10:08:09Z)
• TC-Net: Triple Context Network for Automated Stroke Lesion Segmentation [0.5482532589225552]
  We propose a new network, Triple Context Network (TC-Net), with the capture of spatial contextual information as the core. Our network is evaluated on the open dataset ATLAS, achieving the highest score of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm.
  arXiv  Detail & Related papers  (2022-02-28T11:12:16Z)
• S3Net: 3D LiDAR Sparse Semantic Segmentation Network [1.330528227599978]
  S3Net is a novel convolutional neural network for LiDAR point cloud semantic segmentation. It adopts an encoder-decoder backbone that consists of Sparse Intra-channel Attention Module (SIntraAM) and Sparse Inter-channel Attention Module (SInterAM)
  arXiv  Detail & Related papers  (2021-03-15T22:15:24Z)
• Active Boundary Loss for Semantic Segmentation [58.72057610093194]
  This paper proposes a novel active boundary loss for semantic segmentation. It can progressively encourage the alignment between predicted boundaries and ground-truth boundaries during end-to-end training. Experimental results show that training with the active boundary loss can effectively improve the boundary F-score and mean Intersection-over-Union.
  arXiv  Detail & Related papers  (2021-02-04T15:47:54Z)
• Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds [45.270215729464056]
  Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation. We propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric. GEM is designed to encode geometric information and aggregate features with discrimination in a neighborhood.
  arXiv  Detail & Related papers  (2021-01-07T05:38:19Z)
• JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds [37.703770427574476]
  In this paper, we tackle the 3D semantic edge detection task for the first time. We present a new two-stream fully-convolutional network that jointly performs the two tasks. In particular, we design a joint refinement module that explicitly wires region information and edge information to improve the performances of both tasks.
  arXiv  Detail & Related papers  (2020-07-14T08:00:35Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)

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.