3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection

• URL: http://arxiv.org/abs/2003.00535v1
• Date: Sun, 1 Mar 2020 17:48:17 GMT
• Title: 3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection
• Authors: Liang Du, Jingang Tan, Xiangyang Xue, Lili Chen, Hongkai Wen, Jianfeng Feng, Jiamao Li and Xiaolin Zhang
• Abstract summary: We propose a novel 3D point clouds segmentation framework, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features. Our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost.
• Score: 46.922236354885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost.

Related papers

• LiSD: An Efficient Multi-Task Learning Framework for LiDAR Segmentation and Detection [6.813145466843275]
  LiSD is a voxel-based encoder-decoder framework that addresses both segmentation and detection tasks. It achieves the state-of-the-art performance of 83.3% mIoU on the nuScenes segmentation benchmark for lidar-only methods.
  arXiv  Detail & Related papers  (2024-06-11T07:26:54Z)
• Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation [49.56131393810713]
  We present an SE(3) equivariant architecture and a training strategy to tackle this task in an unsupervised manner. Our method excels in both model performance and computational efficiency, with only 0.25M parameters and 0.92G FLOPs.
  arXiv  Detail & Related papers  (2023-06-08T22:55:32Z)
• PointInst3D: Segmenting 3D Instances by Points [136.7261709896713]
  We propose a fully-convolutional 3D point cloud instance segmentation method that works in a per-point prediction fashion. We find the key to its success is assigning a suitable target to each sampled point. Our approach achieves promising results on both ScanNet and S3DIS benchmarks.
  arXiv  Detail & Related papers  (2022-04-25T02:41:46Z)
• Spatio-Temporal Representation Factorization for Video-based Person Re-Identification [55.01276167336187]
  We propose Spatio-Temporal Representation Factorization module (STRF) for re-ID. STRF is a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-07-25T19:29:37Z)
• VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars [12.343333815270402]
  A unified neural network structure is presented for joint 3D object detection and point cloud segmentation. We leverage rich supervision from both detection and segmentation labels rather than using just one of them.
  arXiv  Detail & Related papers  (2021-07-07T02:16:20Z)
• MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization [61.015704878681795]
  We present a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting are. guaranteeing correspondence and segmentation consistency across multiple input point clouds and. obtaining robust motion-based rigid body segmentation applicable to novel object categories.
  arXiv  Detail & Related papers  (2021-01-17T06:36:28Z)
• Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection [102.62963605429508]
  Point cloud semantic segmentation plays an essential role in autonomous driving. Current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes. We propose a novel Aware 3D Semantic Detection (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task.
  arXiv  Detail & Related papers  (2020-09-22T14:17:40Z)
• Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds [9.434847591440485]
  We build a Bi-Directional Attention module on backbone neural networks for 3D point cloud perception. It uses similarity matrix measured from features for one task to help aggregate non-local information for the other task. From comprehensive experiments and ablation studies on the S3DIS dataset and the PartNet dataset, the superiority of our method is verified.
  arXiv  Detail & Related papers  (2020-03-11T17:16:07Z)

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.