Generating Synthetic RGB-D Datasets for Texture-less Surfaces Reconstruction
Generating Synthetic RGB-D Datasets for Texture-less Surfaces Reconstruction
The state-of-the-art approaches for monocular 3D reconstruction mainly focus on datasets with highly textured images. Most of these methods are trained on datasets like ShapeNet which render well-textured objects. However, in natural scenes, many objects are texture-less, making it difficult to reconstruct them. Unlike textured surfaces, reconstruction of texture-less surfaces has not received as much attention mainly because of a lack of large-scale annotated datasets. Some recent works have also focused on texture-less surfaces, many of which are trained on a small real-world dataset containing 26k images of 5 different texture-less clothing items. To facilitate further research in this direction, we present an extensible dataset generation framework for texture-less RGB-D data. We also make available a large dataset containing 364k images with corresponding groundtruth depth maps and surface normal maps. In addition to clothing items, our dataset includes images of other everyday objects, including animals, furniture, statues, vehicles, and other miscellaneous items. There are 2635 unique 3D models and 48 different objects in total, including 13 main objects from the ShapeNet dataset. Our framework also allows automatic generation of more data from any 3D model, including those obtained directly from the ShapeNet repository. This dataset will aid future research in reconstructing texture-less objects for a wide range of object categories.
| Category | Objects | # of Objects |
|---|---|---|
animals |
asian_dargon, bunny, cats, dragon, duck, pig |
6 |
clothing |
cape, dress, hoodie, jacket, shirt, suit, tracksuit, tshirt |
8 |
furniture |
bed, chair, desk, rocking_chair, sofa, table |
6 |
statues |
armadillo, buddha, lucy, roman, thai_statue |
5 |
misc |
diego, kettle, plants, teapot, skeleton |
5 |
vehicles |
bicycle, car, jeep, ship, spaceship |
5 |
For more details about composition, collection process and data sources, see the dataset homepage.