# EvtSlowTV -- A Large and Diverse Dataset for Event-Based Depth Estimation

arXiv: https://arxiv.org/abs/2511.02953 · HTML: https://mlsanity.com/paper/2511.02953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction](https://mlsanity.com/paper/2102.09320/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Learning Monocular Dense Depth from Events](https://mlsanity.com/paper/2010.08350/) (2020)
- [DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](https://mlsanity.com/paper/2005.08605/) (2020)
- [EventGAN: Leveraging Large Scale Image Datasets for Event Cameras](https://mlsanity.com/paper/1912.01584/) (2019)
- [ESVIO: Event-based Stereo Visual Inertial Odometry](https://mlsanity.com/paper/2212.13184/) (2022)
- [BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation](https://mlsanity.com/paper/2303.07716/) (2023)
- [Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion](https://mlsanity.com/paper/2207.10494/) (2022)
- [Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV](https://mlsanity.com/paper/2307.10713/) (2023)
- [Event-based Stereo Depth Estimation: A Survey](https://mlsanity.com/paper/2409.17680/) (2024)
- [Data-efficient Event Camera Pre-training via Disentangled Masked Modeling](https://mlsanity.com/paper/2403.00416/) (2024)
- [EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching](https://mlsanity.com/paper/2407.21735/) (2024)
- [Secrets of Edge-Informed Contrast Maximization for Event-Based Vision](https://mlsanity.com/paper/2409.14611/) (2024)
- [DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System](https://mlsanity.com/paper/2306.01891/) (2023)
- [On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events](https://mlsanity.com/paper/2412.06359/) (2024)
- [Learning Monocular Depth from Events via Egomotion Compensation](https://mlsanity.com/paper/2412.19067/) (2024)

## Built on by (most cited first, 1 of 1)

- [UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow](https://mlsanity.com/paper/2601.10054/) (2026)

## Abstract (arXiv)

Event cameras, with their high dynamic range (HDR) and low latency, offer a promising alternative for robust depth estimation in challenging environments. However, many event-based depth estimation approaches are constrained by small-scale annotated datasets, limiting their generalizability to real-world scenarios. To bridge this gap, we introduce EvtSlowTV, a large-scale event camera dataset curated from publicly available YouTube footage, which contains more than 13B events across various environmental conditions and motions, including seasonal hiking, flying, scenic driving, and underwater exploration. EvtSlowTV is an order of magnitude larger than existing event datasets, providing an unconstrained, naturalistic setting for event-based depth learning. This work shows the suitability of EvtSlowTV for a self-supervised learning framework to capitalise on the HDR potential of raw event streams. We further demonstrate that training with EvtSlowTV enhances the model's ability to generalise to complex scenes and motions. Our approach removes the need for frame-based annotations and preserves the asynchronous nature of event data.