# Towards End-to-End Neuromorphic Event-based 3D Object Reconstruction Without Physical Priors

arXiv: https://arxiv.org/abs/2501.00741 · HTML: https://mlsanity.com/paper/2501.00741/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Asynchronous Spatial Image Convolutions for Event Cameras](https://mlsanity.com/paper/1812.00438/) (2018)
- [E3D: Event-Based 3D Shape Reconstruction](https://mlsanity.com/paper/2012.05214/) (2020)
- [Event-Based Dense Reconstruction Pipeline](https://mlsanity.com/paper/2203.12270/) (2022)
- [E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning](https://mlsanity.com/paper/2401.08117/) (2024)
- [NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods](https://mlsanity.com/paper/2412.08029/) (2024)
- [EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping](https://mlsanity.com/paper/2312.11911/) (2023)
- [Dense Voxel 3D Reconstruction Using a Monocular Event Camera](https://mlsanity.com/paper/2309.00385/) (2023)

## Built on by (most cited first, 7 of 7)

- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [Ultralight Polarity-Split Neuromorphic SNN for Event-Stream Super-Resolution](https://mlsanity.com/paper/2508.03244/) (2025)
- [A Survey on Event-driven 3D Reconstruction: Development under Different Categories](https://mlsanity.com/paper/2503.19753/) (2025)
- [Exploiting Spatiotemporal Properties for Efficient Event-Driven Human Pose Estimation](https://mlsanity.com/paper/2512.06306/) (2025)
- [MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection](https://mlsanity.com/paper/2506.12697/) (2025)
- [FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics](https://mlsanity.com/paper/2606.26636/) (2026)
- [EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement](https://mlsanity.com/paper/2606.29430/) (2026)

## Abstract (arXiv)

Neuromorphic cameras, also known as event cameras, are asynchronous brightness-change sensors that can capture extremely fast motion without suffering from motion blur, making them particularly promising for 3D reconstruction in extreme environments. However, existing research on 3D reconstruction using monocular neuromorphic cameras is limited, and most of the methods rely on estimating physical priors and employ complex multi-step pipelines. In this work, we propose an end-to-end method for dense voxel 3D reconstruction using neuromorphic cameras that eliminates the need to estimate physical priors. Our method incorporates a novel event representation to enhance edge features, enabling the proposed feature-enhancement model to learn more effectively. Additionally, we introduced Optimal Binarization Threshold Selection Principle as a guideline for future related work, using the optimal reconstruction results achieved with threshold optimization as the benchmark. Our method achieves a 54.6% improvement in reconstruction accuracy compared to the baseline method.