Capturing Fine-Grained Alignments Improves 3D Affordance Detection

• URL: http://arxiv.org/abs/2506.19312v1
• Date: Tue, 24 Jun 2025 04:58:55 GMT
• Title: Capturing Fine-Grained Alignments Improves 3D Affordance Detection
• Abstract summary: We propose LM-AD, a novel method for affordance detection in 3D point clouds.<n>We also introduce the Affordance Query Module (AQM), which efficiently captures fine-grained alignment between point clouds and text.<n>Our method outperformed existing approaches in terms of accuracy and mean Intersection over Union on the 3D AffordanceNet dataset.
• Score: 0.46040036610482665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we address the challenge of affordance detection in 3D point clouds, a task that requires effectively capturing fine-grained alignments between point clouds and text. Existing methods often struggle to model such alignments, resulting in limited performance on standard benchmarks. A key limitation of these approaches is their reliance on simple cosine similarity between point cloud and text embeddings, which lacks the expressiveness needed for fine-grained reasoning. To address this limitation, we propose LM-AD, a novel method for affordance detection in 3D point clouds. Moreover, we introduce the Affordance Query Module (AQM), which efficiently captures fine-grained alignment between point clouds and text by leveraging a pretrained language model. We demonstrated that our method outperformed existing approaches in terms of accuracy and mean Intersection over Union on the 3D AffordanceNet dataset.

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