# Anytime Stereo Image Depth Estimation on Mobile Devices

arXiv: https://arxiv.org/abs/1810.11408 · HTML: https://mlsanity.com/paper/1810.11408/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 30 of 54)

- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [SMD-Nets: Stereo Mixture Density Networks](https://mlsanity.com/paper/2104.03866/) (2021)
- [Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions](https://mlsanity.com/paper/2208.01166/) (2022)
- [Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective](https://mlsanity.com/paper/2203.10887/) (2022)
- [Learning Depth Estimation for Transparent and Mirror Surfaces](https://mlsanity.com/paper/2307.15052/) (2023)
- [OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline](https://mlsanity.com/paper/2312.00343/) (2023)
- [FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation](https://mlsanity.com/paper/2103.04524/) (2021)
- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation](https://mlsanity.com/paper/2406.19833/) (2024)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Anytime Dense Prediction with Confidence Adaptivity](https://mlsanity.com/paper/2104.00749/) (2021)
- [Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints](https://mlsanity.com/paper/2303.02885/) (2023)
- [Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts](https://mlsanity.com/paper/2507.04631/) (2025)
- [TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network](https://mlsanity.com/paper/2211.13755/) (2022)
- [FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks](https://mlsanity.com/paper/2110.02582/) (2021)
- [DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors](https://mlsanity.com/paper/2204.03039/) (2022)
- [Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision](https://mlsanity.com/paper/2302.09922/) (2023)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo](https://mlsanity.com/paper/2310.19583/) (2023)
- [DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications](https://mlsanity.com/paper/2406.10068/) (2024)
- [Stereo Any Video: Temporally Consistent Stereo Matching](https://mlsanity.com/paper/2503.05549/) (2025)
- [LiDAR-Event Stereo Fusion with Hallucinations](https://mlsanity.com/paper/2408.04633/) (2024)
- [Stereo Risk: A Continuous Modeling Approach to Stereo Matching](https://mlsanity.com/paper/2407.03152/) (2024)
- [Separable Convolutions for Optimizing 3D Stereo Networks](https://mlsanity.com/paper/2108.10216/) (2021)
- [Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods](https://mlsanity.com/paper/2209.08305/) (2022)
- [DCVSMNet: Double Cost Volume Stereo Matching Network](https://mlsanity.com/paper/2402.16473/) (2024)
- [NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces](https://mlsanity.com/paper/2506.05815/) (2025)
- [Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming](https://mlsanity.com/paper/2102.10015/) (2021)
- [StereoVAE: A lightweight stereo-matching system using embedded GPUs](https://mlsanity.com/paper/2305.11566/) (2023)
- [Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation](https://mlsanity.com/paper/2404.16386/) (2024)

## Abstract (arXiv)

Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable on power- or memory-constrained devices. Motivated by these shortcomings, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. Depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process 1242 × 375 resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error -- using two orders of magnitude fewer parameters than the most competitive baseline. The source code is available at https://github.com/mileyan/AnyNet .