Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation

• URL: http://arxiv.org/abs/2312.11269v2
• Date: Thu, 4 Jul 2024 10:29:32 GMT
• Title: Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation
• Authors: Sangyun Shin, Kaichen Zhou, Madhu Vankadari, Andrew Markham, Niki Trigoni,
• Abstract summary: We introduce Spherical Mask, a novel coarse-to-fine approach based on spherical representation. Specifically, our coarse detection estimates each instance with a 3D polygon using a center and radial distance predictions. Experimental results from three datasets, such as ScanNetV2, S3DIS, andLS3D, show that our proposed method outperforms existing works.
• Score: 35.875800849174354
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods. We argue that this is due to two limitations: 1) Instance size overestimation by axis-aligned bounding box(AABB) 2) False negative error accumulation from inaccurate box to the refinement phase. In this work, we introduce Spherical Mask, a novel coarse-to-fine approach based on spherical representation, overcoming those two limitations with several benefits. Specifically, our coarse detection estimates each instance with a 3D polygon using a center and radial distance predictions, which avoids excessive size estimation of AABB. To cut the error propagation in the existing coarse-to-fine approaches, we virtually migrate points based on the polygon, allowing all foreground points, including false negatives, to be refined. During inference, the proposal and point migration modules run in parallel and are assembled to form binary masks of instances. We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points, significantly improving the performance. Experimental results from three datasets, such as ScanNetV2, S3DIS, and STPLS3D, show that our proposed method outperforms existing works, demonstrating the effectiveness of the new instance representation with spherical coordinates. The code is available at: https://github.com/yunshin/SphericalMask

Related papers

• Deep Neural Implicit Representation of Accessibility for Multi-Axis Manufacturing [0.0]
  We develop an implicit representation of the collision measure field via deep neural networks (DNNs) We show that our approach is able to accurately interpolate the collision measure from a sparse sampling of rotations, and can represent the collision measure field with a small memory footprint.
  arXiv  Detail & Related papers  (2024-08-30T06:27:25Z)
• CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection [16.021932740447966]
  Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation. We present an unsupervised domain adaptation method that overcomes above difficulties.
  arXiv  Detail & Related papers  (2022-12-01T03:22:55Z)
• CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [55.44204039410225]
  We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels. To recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module.
  arXiv  Detail & Related papers  (2022-10-09T13:38:48Z)
• 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)
• MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation [36.28586460186891]
  This paper studies the 3D instance segmentation problem, which has a variety of real-world applications such as robotics and augmented reality. We propose a novel framework to group and refine the 3D instances. Our approach achieves a 66.4% mAP with the 0.5 IoU threshold on the ScanNetV2 test set, which is 1.9% higher than the state-of-the-art method.
  arXiv  Detail & Related papers  (2022-03-28T11:22:58Z)
• IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment [58.8330387551499]
  We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) We propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2022-03-22T10:14:08Z)
• Dynamic Convolution for 3D Point Cloud Instance Segmentation [146.7971476424351]
  We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. We gather homogeneous points that have identical semantic categories and close votes for the geometric centroids. The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
  arXiv  Detail & Related papers  (2021-07-18T09:05:16Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds [2.924868086534434]
  This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits multiple projections of the point cloud. Our Multi-Projection Fusion framework analyzes spherical and bird's-eye view projections using two separate highly-efficient 2D fully convolutional models.
  arXiv  Detail & Related papers  (2020-11-03T19:40:43Z)
• Robust 6D Object Pose Estimation by Learning RGB-D Features [59.580366107770764]
  We propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-02-29T06:24:55Z)

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.