CrossOver: 3D Scene Cross-Modal Alignment
Authors: name: Sayan Deb Sarkar, name: Ondrej Miksik, name: Marc Pollefeys, name: Daniel Barath, name: Iro Armeni

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 adaptability for real-world
applications in 3D scene understanding.