Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation
Sangyun Shin, Kaichen Zhou, Madhu Vankadari, Andrew Markham, Niki, Trigoni

TL;DR
This paper introduces Spherical Mask, a novel 3D instance segmentation method using spherical representation to improve accuracy by reducing size overestimation and error propagation, outperforming existing approaches.
Contribution
The paper proposes a spherical representation-based coarse-to-fine approach that mitigates size overestimation and false negative errors in 3D instance segmentation.
Findings
Outperforms existing methods on ScanNetV2, S3DIS, and STPLS3D datasets.
Uses spherical coordinates for more accurate instance representation.
Introduces margin-based losses to improve point migration and mask accuracy.
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,…
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Taxonomy
Topics3D Shape Modeling and Analysis · 3D Surveying and Cultural Heritage · Robotics and Sensor-Based Localization
