Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware Synthesis

2023-01-01 · ICCV 2023 1 · Yuwei Yang, Munawar Hayat, Zhao Jin, Hongyuan Zhu, Yinjie Lei

This paper proposes a feature synthesis approach for zero-shot semantic segmentation of 3D point clouds, enabling generalization to previously unseen categories. Given only the class-level semantic information for unseen objects, we strive to enhance the correspondence, alignment and consistency between the visual and semantic spaces, to synthesise diverse, generic and transferable visual features. We develop a masked learning strategy to promote diversity within the same class visual features and enhance the separation between different classes. We further cast the visual features into a prototypical space to model their distribution for alignment with the corresponding semantic space. Finally, we develop a consistency regularizer to preserve the semantic-visual relationships between the real-seen features and synthetic-unseen features. Our approach shows considerable semantic segmentation gains on ScanNet, S3DIS and SemanticKITTI benchmarks. Our code is available at: https://github.com/leolyj/3DPC-GZSL

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Code (1)

leolyj/3dpc-gzsl 공식 구현 pytorch

Tasks

DiversityPoint Cloud Segmentation Segmentation Semantic Segmentation Zero-Shot Semantic Segmentation

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Point Cloud Segmentation Semantic Segmentation Transfer Learning Zero Shot Segmentation+1

GZSL-MoE: Apprentissage G{é}n{é}ralis{é} Z{é}ro-Shot bas{é} sur le M{é}lange d'Experts pour la Segmentation S{é}mantique de Nuages de Points 3DAppliqu{é} {à} un Jeu de Donn{é}es d'Environnement de Collaboration Humain-Robot

2025-09-23 · Ahed Alboody arxiv

Generative Zero-Shot Learning approach (GZSL) has demonstrated significant potential in 3D point cloud semantic segmentation tasks. GZSL leverages generative models like GANs or VAEs to synthesize realistic features (rea…

Generalized Zero-Shot Learning 3D Semantic Segmentation