Work

Cross‐Shape Attention for Part Segmentation of 3D Point Clouds

Year: 2023

Type: article

Abstract: We present a deep learning method that propagates point‐wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross‐shape attention mechanism to enable interactions between a shape's point‐wise features and those of other shapes. The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point‐wise feature representations for shape segmentation. Our method also proposes a shape retrieval measure to select suitable shapes for cross‐shape attention operations for each test shape. Our experiments demonstrate that our approach yields state‐of‐the‐art results in the popular PartNet dataset.

Source: Computer Graphics Forum

DOI: 10.1111/cgf.14909

Authors: Marios Loizou, Siddhant Garg, Dmitry Petrov, Melinos Averkiou, Evangelos Kalogerakis

Institutions: University of Cyprus, University of Massachusetts Amherst

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,710

Funders: Deputy Ministry of Research, Innovation and Digital Policy, Horizon 2020 Framework Programme