35 Gaia Di Lorenzo Computer Vision News Computer Vision News multi-task training, the same representation can support retrieval, localization, and scene alignment. These additional signals, such as text features or scene-graph context, enrich the embedding without compromising its ability to decode back into accurate 3D geometry. In practice, this means a single vector can both describe how an object looks and help reason about where it belongs. Object-X matters because it makes scalable 3D understanding much more practical. Large libraries of objects become manageable, and each object becomes a portable, modular unit that can be inserted into scenes, compared across datasets, or updated with minimal overhead. More broadly, it represents a shift toward objectcentric 3D representations, a direction that simplifies existing pipelines and opens new possibilities in robotics, AR/VR, digital twins, and generative 3D systems. It points toward a future when interacting with 3D objects is as efficient and flexible as working with today’s learned embeddings. Despite its compact size, Object-X reconstructs detailed geometry and appearance comparable to classical 3D representations. Object-X compresses each object into a small, multi-modal embedding that can be fully decoded into a 3D Gaussian Splatting model.
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