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Current state-of-the-art methods for 3D scene understanding are driven by large annotated datasets. To address this, we propose CAD-Estate, a large dataset of complex multi-object RGB videos, each annotated with a globally-consistent 3D representation of its objects, as well as with a room layout, consisting of structural elements in 3D, such as wall, floor, and ceiling.
We annotate each object with a CAD model from a database, and place it in the 3D coordinate frame of the scene with a 9-DoF pose transformation. Our method [1] works on commonly-available RGB videos, without requiring a depth sensor. Many steps are performed automatically, and the tasks performed by humans are simple, well-specified, and require only limited reasoning in 3D. This makes them feasible for crowd-sourcing and has allowed us to construct a large-scale dataset. CAD-Estate offers 101K instances of 12K unique CAD models placed in the 3D representations of 20K videos. The videos of CAD-Estate offer wide complex views of real estate properties. They pose a difficult challenge for automatic scene understanding methods, as they contain numerous objects in each frame, many of which are far from the camera and thus appear small. In comparison to Scan2CAD, the largest existing dataset with CAD model annotations on real scenes, CAD-Estate has 7x more instances and 4x more unique CAD models.
We produce generic 3D room layouts from 2D segmentation masks, which are easy to annotate for humans. Our method [2], automatically reconstructs 3D plane equations and spatial extents for the structural elements from the annotations, and connects adjacent elements at the appropriate contact edges. CAD-Estate offers room layouts for 2246 videos. The videos contain complex topologies, with multiple rooms connected by open doors, multiple floors connected by stairs, and generic geometry with slanted structural elements. Our automatic quality control procedure guarantees high quality of the resulting 3D room layouts.
This dataset contains both datasets that are described in [1], and [2].
Examples of objects dataset [1]
Examples of layouts dataset [2]
You need to first download the dataset and the accompanying source code,
following the instructions here. The text below
assumes that the code lives in ${WORKDIR}/cad_estate and the dataset in
${WORKDIR}/cad_estate/data. Please set the environmental variable WORKDIR first,
according to the instructions.
You can visualize individual scenes with included Jupyter notebooks: objects_notebook.ipynb and room_structure_notebook.ipynb. To start a Jupyter kernel for them, use:
cd ${WORKDIR}/cad_estate/src
jupyter notebookThe kernel requires a CUDA capable GPU.
There are also two PyTorch dataset classes for reading video frames and their object or room structure annotations. You can find more details in the source code
Finally, this file describes the structure of the CAD-Estate annotation files.
If you use the object annotations, please cite [1,3,4]. CAD-Estate contains object annotations [1] that align ShapeNet [3] models over RealEstate10K videos[4]. If you use the 3D room layouts, please cite [2,4]. CAD-Estate contains 3D room layouts [2] over RealEstate10K videos [4].
[1] K.-K. Maninis, S. Popov, M. Nießner, and V. Ferrari. CAD-Estate: Large-scale CAD Model Annotation in RGB Videos. In ICCV, 2023 (to appear).
[2] D. Rozumnyi, S. Popov, K.-K. Maninis, M. Nießner, V. Ferrari. Estimating Generic 3D Room Structures from 2D Annotations. In NeurIPS, 2023.
[3] A. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and Fisher Yu. ShapeNet: An Information-Rich 3D Model Repository. arXiv preprint, 2015.
[4] T. Zhou, R. Tucker, J. Flynn, G. Fyffe, and N. Snavely. Stereo Magnification: Learning view synthesis using multiplane images. In SIGGRAPH, 2018.