arxiv_cv 95% Match Research Paper Robotics Engineers,Computer Vision Engineers,AR/VR Developers,Edge AI Practitioners 3 weeks ago

Online Video Depth Anything: Temporally-Consistent Depth Prediction with Low Memory Consumption

computer-vision › 3d-vision

📄 Abstract

Abstract: Depth estimation from monocular video has become a key component of many real-world computer vision systems. Recently, Video Depth Anything (VDA) has demonstrated strong performance on long video sequences. However, it relies on batch-processing which prohibits its use in an online setting. In this work, we overcome this limitation and introduce online VDA (oVDA). The key innovation is to employ techniques from Large Language Models (LLMs), namely, caching latent features during inference and masking frames at training. Our oVDA method outperforms all competing online video depth estimation methods in both accuracy and VRAM usage. Low VRAM usage is particularly important for deployment on edge devices. We demonstrate that oVDA runs at 42 FPS on an NVIDIA A100 and at 20 FPS on an NVIDIA Jetson edge device. We will release both, code and compilation scripts, making oVDA easy to deploy on low-power hardware.

Key Contributions

This paper presents online Video Depth Anything (oVDA), enabling temporally-consistent depth prediction from monocular video in an online setting with low memory consumption. It innovatively applies LLM techniques like latent feature caching and frame masking to overcome the batch-processing limitations of previous methods, making it suitable for edge devices.

Business Value

Enables real-time, accurate depth perception for applications on resource-constrained devices like drones, mobile robots, and AR/VR headsets, significantly expanding the possibilities for mobile AI applications.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Very High, specifically designed for low VRAM usage and edge device deployment, with provided code and compilation scripts.

Limitations Addressed

The inability of Video Depth Anything (VDA) to operate in an online setting due to its batch-processing nature, and its high VRAM usage, which prevents deployment on edge devices.

Performance Gains

Outperforms competing online methods in accuracy and VRAM usage,Runs at 42 FPS on NVIDIA A100,Runs at 20 FPS on NVIDIA Jetson edge device

View Code on GitHub

Technical Tags

monocular depth estimationvideo depth predictiononline processingtemporal consistencylow memory consumptionedge deviceslatent feature cachingframe maskingLLM techniques

Research Topics

Depth EstimationVideo UnderstandingComputer VisionEdge AIDeep Learning

Methods & Architectures

Latent feature cachingFrame maskingTechniques adapted from Large Language Models (LLMs) Video Depth Anything (VDA)

Applications & Tasks

Robotics Autonomous Driving Augmented Reality Virtual Reality 3D Reconstruction Batch Processing LimitationOnline Processing RequirementHigh VRAM UsageTemporal Consistency Online Video Depth EstimationTemporally-Consistent Depth Prediction

Related Fields

Computer VisionRoboticsDeep LearningEdge ComputingLarge Language Models

Keywords

depth estimationmonocular videoonline processingtemporal consistencyedge AIlow memoryLLMVDAreal-timecomputer vision3D visionfeature caching

Academic Context

#Depth Estimation#Video Understanding#Computer Vision#Edge AI#Deep Learning

Technology Stack

Frameworks & Libraries

Video Depth Anything (VDA)

Commercial Potential

Potential Products

Real-time depth sensing modules for robots and dronesAR/VR applications with enhanced spatial awarenessEfficient depth estimation libraries for edge devices

Target Industries

RoboticsAutomotiveAR/VRConsumer ElectronicsLogistics

Use Case Examples

Enabling a robot vacuum cleaner to navigate and map a home in real-time using its camera.Providing depth information for mobile AR applications on smartphones.Facilitating obstacle avoidance for drones during flight.

Competitive Edge

Achieves online processing and low memory consumption for video depth estimation, outperforming previous methods and enabling deployment on edge hardware.

Market Opportunity

Massive and growing market for edge AI and computer vision solutions.

Revenue Models

Licensing of the technologyintegration into hardware/software productscloud-based inference services.

Resource Requirements

Compute Needs

Optimized for low VRAM usage, suitable for edge devices (e.g., NVIDIA Jetson) and efficient on high-end GPUs (e.g., A100).

Data Requirements

Requires video datasets for training and evaluation.

Deployment Constraints

Limited by the processing power of edge devices. Ensuring robustness across diverse video conditions.

Scalability

The use of LLM techniques like caching and masking aids in efficient processing, suggesting good scalability for real-time applications.

Regulatory Considerations

None directly mentionedbut applications in autonomous systems may face regulatory scrutiny.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-2 years

Licensing

Likely open-source (given code release).

Patent Potential

High, for the novel application of LLM techniques to online video depth estimation and the resulting efficiency gains.

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