ReviewSensors (Basel, Switzerland)2022
Analytical Review of Event-Based Camera Depth Estimation Methods and Systems.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Self-supervised cross frame-rate event-frame depth learning without ground truth.Scientific reports · 2026Article
- Neuromorphic detection and cooling of microparticles in arrays.Nature communications · 2025Article
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- Sensor-Fusion Based Navigation for Autonomous Mobile Robot.Sensors (Basel, Switzerland) · 2025Review
- Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation.Sensors (Basel, Switzerland) · 2024Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- A neuromorphic event data interpretation approach with hardware reservoir.Frontiers in neuroscience · 2024Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation.Sensors (Basel, Switzerland) · 2022Article
- EVtracker: An Event-Driven Spatiotemporal Method for Dynamic Object Tracking.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Event-based cameras have increasingly become more commonplace in the commercial space as the performance of these cameras has also continued to increase to the degree where they can exponentially outperform their frame-based counterparts in many applications. However, instantiations of event-based cameras for depth estimation are sparse. After a short introduction detailing the salient differences and features of an event-based camera compared to that of a traditional, frame-based one, this work summarizes the published event-based methods and systems known to date. An analytical review of these methods and systems is performed, justifying the conclusions drawn. This work is concluded with insights and recommendations for further development in the field of event-based camera depth estimation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.