# UniCT Depth: Event-Image Fusion Based Monocular Depth Estimation with Convolution-Compensated ViT Dual SA Block

arXiv: https://arxiv.org/abs/2507.19948 · HTML: https://mlsanity.com/paper/2507.19948/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 26 of 26)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [DaViT: Dual Attention Vision Transformers](https://mlsanity.com/paper/2204.03645/) (2022)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction](https://mlsanity.com/paper/2102.09320/) (2021)
- [Learning Monocular Dense Depth from Events](https://mlsanity.com/paper/2010.08350/) (2020)
- [IEBins: Iterative Elastic Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2309.14137/) (2023)
- [Event Transformer. A sparse-aware solution for efficient event data processing](https://mlsanity.com/paper/2204.03355/) (2022)
- [Hierarchical Neural Memory Network for Low Latency Event Processing](https://mlsanity.com/paper/2305.17852/) (2023)
- [Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation](https://mlsanity.com/paper/2109.01801/) (2021)
- [Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey](https://mlsanity.com/paper/2001.02319/) (2020)
- [Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More](https://mlsanity.com/paper/1904.10167/) (2019)
- [Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision](https://mlsanity.com/paper/2103.12209/) (2021)
- [EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions](https://mlsanity.com/paper/2302.03860/) (2023)
- [Event Transformer+. A multi-purpose solution for efficient event data processing](https://mlsanity.com/paper/2211.12222/) (2022)
- [SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events](https://mlsanity.com/paper/2309.12842/) (2023)
- [GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2411.13147/) (2024)

## Built on by (most cited first, 3 of 3)

- [LiFR v2: Completion-Augmented Event Propagation for High-Rate Dense Prediction](https://mlsanity.com/paper/2609.25803/) (2026)
- [Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation](https://mlsanity.com/paper/2610.03439/) (2026)
- [UniDynamics: Event-RGB Fusion for Unified Future 4D Dynamic Scene Generation](https://mlsanity.com/paper/2610.03473/) (2026)

## Abstract (arXiv)

Depth estimation plays a crucial role in 3D scene understanding and is extensively used in a wide range of vision tasks. Image-based methods struggle in challenging scenarios, while event cameras offer high dynamic range and temporal resolution but face difficulties with sparse data. Combining event and image data provides significant advantages, yet effective integration remains challenging. Existing CNN-based fusion methods struggle with occlusions and depth disparities due to limited receptive fields, while Transformer-based fusion methods often lack deep modality interaction. To address these issues, we propose UniCT Depth, an event-image fusion method that unifies CNNs and Transformers to model local and global features. We propose the Convolution-compensated ViT Dual SA (CcViT-DA) Block, designed for the encoder, which integrates Context Modeling Self-Attention (CMSA) to capture spatial dependencies and Modal Fusion Self-Attention (MFSA) for effective cross-modal fusion. Furthermore, we design the tailored Detail Compensation Convolution (DCC) Block to improve texture details and enhances edge representations. Experiments show that UniCT Depth outperforms existing image, event, and fusion-based monocular depth estimation methods across key metrics.