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 4,672 frames captured with a depth camera. Our comprehensive benchmarks demonstrate 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. The dataset is publicly available at https://github.com/saifkhichi96/Shape25D.

翻译：在计算机视觉中，重建无纹理表面带来了独特的挑战，这主要是由于缺乏专门的数据集来满足在缺乏纹理信息情况下进行深度和法线估计的细微需求。我们推出了“Shape2.5D”，这是一个新颖的大规模数据集，旨在填补这一空白。我们的数据集包含超过39,772个3D模型和48个独特物体的117万帧图像，为无纹理物体重建提供了深度图和表面法线图。所提出的数据集包含使用3D建模软件渲染的合成图像，以模拟各种光照条件和视角。它还包括一个包含4,672帧的真实世界子集，这些帧由深度相机捕获。我们全面的基准测试证明了该数据集能够支持开发从RGB图像中稳健估计深度和法线并进行体素重建的算法。我们开源的数据生成流程允许该数据集为未来的研究进行扩展和适配。该数据集公开于 https://github.com/saifkhichi96/Shape25D。