XDen-1K: A Density Field Dataset of Real-World Objects

A deep understanding of the physical world is essential forrobotic manipulation and physically realistic simulation. While currentmethods, including VLM-based and other learning-based approaches,have shown promise in physical property inference, their evaluation isoften hindered by the lack of physically grounded reference data. Toaddress this gap, we introduce XDen-1K, the first large-scale multi-modal dataset that provides physically grounded density field for real-world objects. XDen-1K comprises 1,000 real-world objects spanning137 categories, with comprehensive data for each object, including ahigh-resolution, carefully curated 3D geometric model with part-levelannotations and paired real-world biplanar X-ray scans. In addition,XDen-1K includes high-fidelity volumetric density field reconstructedfrom sparse biplanar X-ray views via a novel optimization framework.XDen-1K also provides a benchmark for density estimation and enablesX-ray-conditioned volumetric segmentation. Experiments further demon-strate that the center-of-mass prior derived by XDen-1K can improverobotic manipulation performance. By providing real-world X-ray scansand physics-consistent density field, XDen-1K establishes a foundationfor advancing physical property inference and embodied AI. Additionaldetails and dataset resources are available at https://xden-1k.github.io/ and https://huggingface.co/datasets/zhangjxx/XDen-1K.