Computer Vision News Computer Vision News 34 NeurIPS Paper Gaia Di Lorenzo recently graduated from ETH Zurich with a Master’s in Computer Science (major in Machine Intelligence), and her thesis - Object-X - has been accepted to NeurIPS 2025, an exciting milestone at the start of her research career. She has now joined NVIDIA, where she works on AI Agents, focusing on how intelligent, embodied systems can understand, interact with, and reason about the world. Congratulations, Gaia! In 3D vision, objects are often represented through point clouds, meshes, or neural fields such as NeRFs or 3D Gaussian Splatting (3DGS). These representations achieve strong results, but they are far from lightweight. When dealing with thousands of objects, a realistic scenario in robotics, AR/VR, simulation, or large-scale scene understanding, storage and computation quickly become major bottlenecks. Object-X, developed at ETH Zurich, introduces a more scalable alternative: object-centric embeddings that are both compact and decodable. Each object is reduced to a single, fixedsize vector that requires 3-4 orders of magnitude less storage than common 3D formats, while still retaining enough information to reconstruct the object with high visual fidelity. The pipeline begins by canonicalizing each segmented object and voxelizing it into a regular 3D grid. Using posed images, multi-view features are then projected into the voxels, creating a dense 3D field of learned signals. A 3D encoder compresses this representation into SLat, a structured latent that organizes geometry and appearance. SLat is then further compressed into the U-3DGS embedding, a compact vector that is easy to store, share, or index. From this tiny embedding, Object-X can decode a full 3D Gaussian Splatting model, effectively turning a small vector back into a high-quality 3D object ready for rendering, mesh extraction, editing, or alignment. One of the strengths of Object-X is that this embedding is not only compact but also versatile. Through
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