# Panoramas from Photons

arXiv: https://arxiv.org/abs/2309.03811 · HTML: https://mlsanity.com/paper/2309.03811/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Deep Lucas-Kanade Homography for Multimodal Image Alignment](https://mlsanity.com/paper/2104.11693/) (2021)
- [A megapixel time-gated SPAD image sensor for 2D and 3D imaging applications](https://mlsanity.com/paper/1912.12910/) (2019)
- [Quanta Burst Photography](https://mlsanity.com/paper/2006.11840/) (2020)
- [High Flux Passive Imaging with Single-Photon Sensors](https://mlsanity.com/paper/1902.10190/) (2019)
- [MBA-VO: Motion Blur Aware Visual Odometry](https://mlsanity.com/paper/2103.13684/) (2021)
- [Passive Inter-Photon Imaging](https://mlsanity.com/paper/2104.00059/) (2021)
- [Motion Adaptive Deblurring with Single-Photon Cameras](https://mlsanity.com/paper/2012.07931/) (2020)

## Built on by (most cited first, 2 of 2)

- [PhotonSplat: 3D Scene Reconstruction and Colorization from SPAD Sensors](https://mlsanity.com/paper/2506.21680/) (2025)
- [Accurate Simulation Pipeline for Passive Single-Photon Imaging](https://mlsanity.com/paper/2601.12850/) (2026)

## Abstract (arXiv)

Scene reconstruction in the presence of high-speed motion and low illumination is important in many applications such as augmented and virtual reality, drone navigation, and autonomous robotics. Traditional motion estimation techniques fail in such conditions, suffering from too much blur in the presence of high-speed motion and strong noise in low-light conditions. Single-photon cameras have recently emerged as a promising technology capable of capturing hundreds of thousands of photon frames per second thanks to their high speed and extreme sensitivity. Unfortunately, traditional computer vision techniques are not well suited for dealing with the binary-valued photon data captured by these cameras because these are corrupted by extreme Poisson noise. Here we present a method capable of estimating extreme scene motion under challenging conditions, such as low light or high dynamic range, from a sequence of high-speed image frames such as those captured by a single-photon camera. Our method relies on iteratively improving a motion estimate by grouping and aggregating frames after-the-fact, in a stratified manner. We demonstrate the creation of high-quality panoramas under fast motion and extremely low light, and super-resolution results using a custom single-photon camera prototype. For code and supplemental material see our hrefhttps://wisionlab.com/project/panoramas-from-photons/project webpage.