CrossOver: 3D Scene Cross-Modal Alignment

• URL: http://arxiv.org/abs/2502.15011v2
• Date: Fri, 04 Apr 2025 18:15:59 GMT
• Title: CrossOver: 3D Scene Cross-Modal Alignment
• Authors: Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni,
• Abstract summary: CrossOver is a novel framework for cross-modal 3D scene understanding.<n>It learns a unified, modality-agnostic embedding space for scenes by aligning modalities.<n>It supports robust scene retrieval and object localization, even with missing modalities.
• Score: 78.3057713547313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal 3D object understanding has gained significant attention, yet current approaches often assume complete data availability and rigid alignment across all modalities. We present CrossOver, a novel framework for cross-modal 3D scene understanding via flexible, scene-level modality alignment. Unlike traditional methods that require aligned modality data for every object instance, CrossOver learns a unified, modality-agnostic embedding space for scenes by aligning modalities -- RGB images, point clouds, CAD models, floorplans, and text descriptions -- with relaxed constraints and without explicit object semantics. Leveraging dimensionality-specific encoders, a multi-stage training pipeline, and emergent cross-modal behaviors, CrossOver supports robust scene retrieval and object localization, even with missing modalities. Evaluations on ScanNet and 3RScan datasets show its superior performance across diverse metrics, highlighting the adaptability for real-world applications in 3D scene understanding.

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