GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

• URL: http://arxiv.org/abs/2108.08401v1
• Date: Wed, 18 Aug 2021 21:49:58 GMT
• Title: GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
• Authors: Ryan Razani, Ran Cheng, Enxu Li, Ehsan Taghavi, Yuan Ren, and Liu Bingbing
• Abstract summary: GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects. Our new design consists of a novel instance-level network to process the semantic results. Extensive experiments demonstrate that GP-S3Net outperforms the current state-of-the-art approaches.
• Score: 1.9949920338542213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In this paper, we propose a new computationally efficient LiDAR based panoptic segmentation framework, called GP-S3Net. GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information. Our new design consists of a novel instance-level network to process the semantic results by constructing a graph convolutional network to identify objects (foreground), which later on are fused with the background classes. Through the fine-grained clusters of the foreground objects from the semantic segmentation backbone, over-segmentation priors are generated and subsequently processed by 3D sparse convolution to embed each cluster. Each cluster is treated as a node in the graph and its corresponding embedding is used as its node feature. Then a GCNN predicts whether edges exist between each cluster pair. We utilize the instance label to generate ground truth edge labels for each constructed graph in order to supervise the learning. Extensive experiments demonstrate that GP-S3Net outperforms the current state-of-the-art approaches, by a significant margin across available datasets such as, nuScenes and SemanticPOSS, ranking first on the competitive public SemanticKITTI leaderboard upon publication.

Related papers

• Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration [22.308070598885532]
  We treat the point cloud registration problem as a semantic instance matching and registration task. We propose a deep semantic graph matching method (DeepSGM) for large-scale outdoor point cloud registration. Experimental results conducted on the KITTI Odometry dataset demonstrate that the proposed method improves the registration performance.
  arXiv  Detail & Related papers  (2023-08-10T03:07:28Z)
• Local Structure-aware Graph Contrastive Representation Learning [12.554113138406688]
  We propose a Local Structure-aware Graph Contrastive representation Learning method (LS-GCL) to model the structural information of nodes from multiple views. For the local view, the semantic subgraph of each target node is input into a shared GNN encoder to obtain the target node embeddings at the subgraph-level. For the global view, considering the original graph preserves indispensable semantic information of nodes, we leverage the shared GNN encoder to learn the target node embeddings at the global graph-level.
  arXiv  Detail & Related papers  (2023-08-07T03:23:46Z)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• A Variational Edge Partition Model for Supervised Graph Representation Learning [51.30365677476971]
  This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities. We partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN based predictor that combines community-specific GNNs for the end classification task.
  arXiv  Detail & Related papers  (2022-02-07T14:37:50Z)
• CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds [2.891413712995641]
  We propose a novel real-time end-to-end panoptic segmentation network for LiDAR point clouds, called CPSeg. CPSeg comprises a shared encoder, a dual decoder, a task-aware attention module (TAM) and a cluster-free instance segmentation head.
  arXiv  Detail & Related papers  (2021-11-02T16:44:06Z)
• PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation [0.9137554315375922]
  We propose a inception based deep network architecture called PIG-Net, that effectively characterizes the local and global geometric details of the point clouds. We perform an exhaustive experimental analysis of the PIG-Net architecture on two state-of-the-art datasets.
  arXiv  Detail & Related papers  (2021-01-28T13:27:55Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation [70.97625552643493]
  This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. We propose a novel graph neuralS network (TG-Net) which captures the local contexts by utilizing all proposals.
  arXiv  Detail & Related papers  (2020-12-10T07:57:44Z)
• Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
  We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN) GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
  arXiv  Detail & Related papers  (2020-06-23T04:13:23Z)
• GPS-Net: Graph Property Sensing Network for Scene Graph Generation [91.60326359082408]
  Scene graph generation (SGG) aims to detect objects in an image along with their pairwise relationships. GPS-Net fully explores three properties for SGG: edge direction information, the difference in priority between nodes, and the long-tailed distribution of relationships. GPS-Net achieves state-of-the-art performance on three popular databases: VG, OI, and VRD by significant gains under various settings and metrics.
  arXiv  Detail & Related papers  (2020-03-29T07:22:31Z)

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.