Shape25D: A Dataset of Texture-less Surfaces for Depth and Normals Estimation
<|reference_start|>Shape25D: A Dataset of Texture-less Surfaces for Depth and Normals Estimation: Reconstructing texture-less surfaces poses unique challenges in computer vision, primarily due to the lack of specialized datasets that cater to the nuanced needs of depth and normals estimation in the absence of textural information. We introduce "Shape2.5D," a novel, large-scale dataset designed to address this gap. Comprising 364k frames spanning 2635 3D models and 48 unique objects, our dataset provides depth and surface normal maps for texture-less object reconstruction. The proposed dataset includes synthetic images rendered with 3D modeling software to simulate various lighting conditions and viewing angles. It also includes a real-world subset comprising 4672 frames captured with a depth camera. Our comprehensive benchmarks, performed using a modified encoder-decoder network, showcase the dataset's capability to support the development of algorithms that robustly estimate depth and normals from RGB images. Our open-source data generation pipeline allows the dataset to be extended and adapted for future research. The dataset is publicly available at \url{https://github.com/saifkhichi96/Shape25D}.<|reference_end|>
@article{khan2024shape2.5d:,
    title={Shape2.5D: A Dataset of Texture-less Surfaces for Depth and Normals
  Estimation},
    author={Muhammad Saif Ullah Khan, Sankalp Sinha, Didier Stricker, Marcus
  Liwicki, Muhammad Zeshan Afzal},
    journal={arXiv preprint arXiv:2406.15831},
    year={2024},
    doi={10.1109/ACCESS.2024.3492703},
    archivePrefix={arXiv},
    eprint={2406.15831},
    primaryClass={cs.CV}
}