Shape2.5D Dataset

sellDataset Paper sellIEEE Access sellTexture-less Surfaces sellDepth Estimation sellNormal Estimation

Shape2.5D: A Dataset of Texture-less Surfaces for Depth and Normals Estimation

Muhammad Saif Ullah Khan, Sankalp Sinha, Didier Stricker, Marcus Liwicki, Muhammad Zeshan Afzal
Paper Supplementary Code arXiv
Shape2.5D: A Dataset of Texture-less Surfaces for Depth and Normals Estimation

The dataset features several common 3D objects rendered as images in Blender with no textures and different lights. Depth and normal maps are provided.

Abstract

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 1.17 million frames spanning over 39,772 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 showcase the dataset’s ability to support the development of algorithms that robustly estimate depth and normals from RGB images, and perform voxel reconstruction. Our open-source data generation pipeline allows the dataset to be extended and adapted for future research.

TL;DR

We introduce a novel dataset, Shape2.5D:

  1. For depth and normals estimation on texture-less surfaces
  2. 302k synthetic frames for 35 3D models
  3. 62k more synthetic frames for 2600 3D models of 13 common ShapeNet objects
  4. 4672 real-world frames for 6 clothing and household items

Benchmarks using an encoder-decoder network for depth and normals estimation.