  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}.
