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

arXiv: https://arxiv.org/abs/2406.15831 · HTML: https://mlsanity.com/paper/2406.15831/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 52 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling](https://mlsanity.com/paper/1804.04610/) (2018)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation](https://mlsanity.com/paper/2203.09516/) (2022)
- [Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images](https://mlsanity.com/paper/1901.11153/) (2019)
- [MarrNet: 3D Shape Reconstruction via 2.5D Sketches](https://mlsanity.com/paper/1711.03129/) (2017)
- [Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images](https://mlsanity.com/paper/2006.12250/) (2020)
- [Designing Deep Networks for Surface Normal Estimation](https://mlsanity.com/paper/1411.4958/) (2014)
- [Robust Monocular Depth Estimation under Challenging Conditions](https://mlsanity.com/paper/2308.09711/) (2023)
- [3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow](https://mlsanity.com/paper/2203.15190/) (2022)
- [Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture](https://mlsanity.com/paper/2311.00457/) (2023)
- [Learning Depth Estimation for Transparent and Mirror Surfaces](https://mlsanity.com/paper/2307.15052/) (2023)
- [SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images](https://mlsanity.com/paper/2008.05742/) (2020)
- [Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction](https://mlsanity.com/paper/2204.03642/) (2022)
- [FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction](https://mlsanity.com/paper/2205.07763/) (2022)
- [Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance](https://mlsanity.com/paper/2105.11599/) (2021)
- [IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/1904.12144/) (2019)
- [HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model](https://mlsanity.com/paper/1803.10193/) (2018)
- [3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers](https://mlsanity.com/paper/2110.08861/) (2021)
- [Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors](https://mlsanity.com/paper/2209.15383/) (2022)
- [The Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2211.12174/) (2022)
- [Learning to Reconstruct Texture-less Deformable Surfaces from a Single View](https://mlsanity.com/paper/1803.08908/) (2018)
- [Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network](https://mlsanity.com/paper/2208.00183/) (2022)
- [3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction](https://mlsanity.com/paper/2205.14575/) (2022)
- [Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization](https://mlsanity.com/paper/2010.08276/) (2020)

## Abstract (arXiv)

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.