Learning Human Visual Attention on 3D Surfaces through Geometry-Queried Semantic Priors

• URL: http://arxiv.org/abs/2602.06419v1
• Date: Fri, 06 Feb 2026 06:15:38 GMT
• Title: Learning Human Visual Attention on 3D Surfaces through Geometry-Queried Semantic Priors
• Authors: Soham Pahari, Sandeep C. Kumain,
• Abstract summary: We introduce SemGeo-AttentionNet, a dual-stream architecture that formalizes the interplay between geometric processing and semantic recognition.<n>We extend our framework to temporal scanpath generation through reinforcement learning.<n> Evaluation on SAL3D, NUS3D and 3DVA datasets demonstrates substantial improvements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human visual attention on three-dimensional objects emerges from the interplay between bottom-up geometric processing and top-down semantic recognition. Existing 3D saliency methods rely on hand-crafted geometric features or learning-based approaches that lack semantic awareness, failing to explain why humans fixate on semantically meaningful but geometrically unremarkable regions. We introduce SemGeo-AttentionNet, a dual-stream architecture that explicitly formalizes this dichotomy through asymmetric cross-modal fusion, leveraging diffusion-based semantic priors from geometry-conditioned multi-view rendering and point cloud transformers for geometric processing. Cross-attention ensures geometric features query semantic content, enabling bottom-up distinctiveness to guide top-down retrieval. We extend our framework to temporal scanpath generation through reinforcement learning, introducing the first formulation respecting 3D mesh topology with inhibition-of-return dynamics. Evaluation on SAL3D, NUS3D and 3DVA datasets demonstrates substantial improvements, validating how cognitively motivated architectures effectively model human visual attention on three-dimensional surfaces.

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