# Surface Normals in the Wild

arXiv: https://arxiv.org/abs/1704.02956 · HTML: https://mlsanity.com/paper/1704.02956/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2017.

## Built on by (most cited first, 8 of 8)

- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction](https://mlsanity.com/paper/2103.12091/) (2021)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [Egocentric Scene Understanding via Multimodal Spatial Rectifier](https://mlsanity.com/paper/2207.07077/) (2022)
- [Scale-Invariant Monocular Depth Estimation via SSI Depth](https://mlsanity.com/paper/2406.09374/) (2024)
- [JRN-Geo: A Joint Perception Network based on RGB and Normal images for Cross-view Geo-localization](https://mlsanity.com/paper/2509.05696/) (2025)
- [Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding](https://mlsanity.com/paper/2212.05040/) (2022)

## Abstract (arXiv)

We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth and our own dataset demonstrate that our approach can significantly improve the quality of depth estimation in the wild.