# DiffuserCam: Lensless Single-exposure 3D Imaging

arXiv: https://arxiv.org/abs/1710.02134 · HTML: https://mlsanity.com/paper/1710.02134/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

## Built on by (most cited first, 30 of 51)

- [Untrained networks for compressive lensless photography](https://mlsanity.com/paper/2103.07609/) (2021)
- [Thin On-Sensor Nanophotonic Array Cameras](https://mlsanity.com/paper/2308.02797/) (2023)
- [PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging](https://mlsanity.com/paper/2409.17996/) (2024)
- [Unrolled Primal-Dual Networks for Lensless Cameras](https://mlsanity.com/paper/2203.04353/) (2022)
- [Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging](https://mlsanity.com/paper/2204.09871/) (2022)
- [Learning Lens Blur Fields](https://mlsanity.com/paper/2310.11535/) (2023)
- [A Differentiable Wave Optics Model for End-to-End Computational Imaging System Optimization](https://mlsanity.com/paper/2412.09774/) (2024)
- [LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction](https://mlsanity.com/paper/2505.01755/) (2025)
- [Minimalist Vision with Freeform Pixels](https://mlsanity.com/paper/2501.00142/) (2024)
- [Degradation-Modeled Multipath Diffusion for Tunable Metalens Photography](https://mlsanity.com/paper/2506.22753/) (2025)
- [A Simple Framework for 3D Lensless Imaging with Programmable Masks](https://mlsanity.com/paper/2108.07966/) (2021)
- [Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction](https://mlsanity.com/paper/2502.01102/) (2025)
- [FourierNets enable the design of highly non-local optical encoders for computational imaging](https://mlsanity.com/paper/2104.10611/) (2021)
- [Privacy-Enhancing Optical Embeddings for Lensless Classification](https://mlsanity.com/paper/2211.12864/) (2022)
- [LPSNet: End-to-End Human Pose and Shape Estimation with Lensless Imaging](https://mlsanity.com/paper/2404.01941/) (2024)
- [FlatTrack: Eye-tracking with ultra-thin lensless cameras](https://mlsanity.com/paper/2501.15450/) (2025)
- [Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues](https://mlsanity.com/paper/2505.17358/) (2025)
- [Diffractive lensless imaging with optimized Voronoi-Fresnel phase](https://mlsanity.com/paper/2109.13703/) (2021)
- [Angle Sensitive Pixels for Lensless Imaging on Spherical Sensors](https://mlsanity.com/paper/2306.15953/) (2023)
- [LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification](https://mlsanity.com/paper/2406.04129/) (2024)
- [GANESH: Generalizable NeRF for Lensless Imaging](https://mlsanity.com/paper/2411.04810/) (2024)
- [Enabling High-Quality In-the-Wild Imaging from Severely Aberrated Metalens Bursts](https://mlsanity.com/paper/2510.10083/) (2025)
- [Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography](https://mlsanity.com/paper/2108.05563/) (2021)
- [Coded Illumination for Improved Lensless Imaging](https://mlsanity.com/paper/2111.12862/) (2021)
- [Learning rich optical embeddings for privacy-preserving lensless image classification](https://mlsanity.com/paper/2206.01429/) (2022)
- [Text detection and recognition based on a lensless imaging system](https://mlsanity.com/paper/2210.04244/) (2022)
- [mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics](https://mlsanity.com/paper/2303.16205/) (2023)
- [Single-sample image-fusion upsampling of fluorescence lifetime images](https://mlsanity.com/paper/2404.13102/) (2024)
- [Super-Resolution with Structured Motion](https://mlsanity.com/paper/2505.15961/) (2025)
- [Collaborative On-Sensor Array Cameras](https://mlsanity.com/paper/2506.04061/) (2025)

## Abstract (arXiv)

We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging. Our lensless system consists solely of a diffuser placed in front of a standard image sensor. Every point within the volumetric field-of-view projects a unique pseudorandom pattern of caustics on the sensor. By using a physical approximation and simple calibration scheme, we solve the large-scale inverse problem in a computationally efficient way. The caustic patterns enable compressed sensing, which exploits sparsity in the sample to solve for more 3D voxels than pixels on the 2D sensor. Our 3D voxel grid is chosen to match the experimentally measured two-point optical resolution across the field-of-view, resulting in 100 million voxels being reconstructed from a single 1.3 megapixel image. However, the effective resolution varies significantly with scene content. Because this effect is common to a wide range of computational cameras, we provide new theory for analyzing resolution in such systems.