# Extended monocular 3D imaging

arXiv: https://arxiv.org/abs/2502.07403 · HTML: https://mlsanity.com/paper/2502.07403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields](https://mlsanity.com/paper/1502.07411/) (2015)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts](https://mlsanity.com/paper/2406.03461/) (2024)
- [PS2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing](https://mlsanity.com/paper/2207.00945/) (2022)
- [Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second](https://mlsanity.com/paper/2301.08351/) (2023)

## Abstract (arXiv)

3D vision is of paramount importance for numerous applications ranging from machine intelligence to precision metrology. Despite much recent progress, the majority of 3D imaging hardware remains bulky and complicated and provides much lower image resolution compared to their 2D counterparts. Moreover, there are many well-known scenarios that existing 3D imaging solutions frequently fail. Here, we introduce an extended monocular 3D imaging (EM3D) framework that fully exploits the vectorial wave nature of light. Via the multi-stage fusion of diffraction- and polarization-based depth cues, using a compact monocular camera equipped with a diffractive-refractive hybrid lens, we experimentally demonstrate the snapshot acquisition of a million-pixel and accurate 3D point cloud for extended scenes that are traditionally challenging, including those with low texture, being highly reflective, or nearly transparent, without a data prior. Furthermore, we discover that the combination of depth and polarization information can unlock unique new opportunities in material identification, which may further expand machine intelligence for applications like target recognition and face anti-spoofing. The straightforward yet powerful architecture thus opens up a new path for a higher-dimensional machine vision in a minimal form factor, facilitating the deployment of monocular cameras for applications in much more diverse scenarios.