Towards Scaling Marine Perception with Synthetic Data
Improving marine perception with OceanSim's synthetic data generation pipeline.
Key Findings
Methodology
This study extends the IsaacSim-based OceanSim simulator with a Synthetic Data Generation (SDG) pipeline for training models in marine environments. The pipeline allows users to generate large, automatically labeled photorealistic datasets with configurable scene appearance, structure, and sensor settings.
Key Results
- In a sea urchin detection task, models trained with synthetic data performed well on real datasets, with YOLO achieving a precision of 0.521 and recall of 0.389.
- Simulations with varied scene complexity and distractors significantly improved model generalization.
- Increasing the synthetic dataset size did not significantly enhance performance, indicating diversity is more crucial than quantity.
Significance
This research addresses the data scarcity issue in marine perception by providing a cost-effective solution through synthetic data generation, advancing underwater robotic perception technology. It offers a reference for synthetic data applications in other fields.
Technical Contribution
Technical contributions include developing a highly configurable synthetic data generation pipeline, supporting semantic annotations across multiple sensor modalities, enhancing simulator perceptual fidelity and scene diversity.
Novelty
This study is the first to achieve high-fidelity synthetic data generation in marine perception, significantly narrowing the sim-to-real gap.
Limitations
- The current light transport model simplifies underwater optical effects, failing to fully simulate real-world light transport characteristics.
- Scene diversity is limited by the number of underlying assets, affecting data breadth.
Future Work
Future work will focus on improving underwater optical models, increasing scene diversity, and developing broader real-world benchmarks to better assess model generalization.
AI Executive Summary
Scaling machine learning in marine environments faces data scarcity. Traditional real-world data collection is costly and time-consuming, limiting marine perception technology. This paper proposes a synthetic data generation pipeline based on OceanSim, capable of automatically generating large-scale, well-labeled photorealistic datasets for model training in underwater scenarios.
The method generates diverse synthetic data by configuring scene appearance, structure, and sensor settings, validated in a sea urchin detection task. Results show synthetic data effectively enhances model performance on real data, especially in complex backgrounds and with distractors.
However, current optical models and scene diversity need improvement. Future work will focus on enhancing simulator optical fidelity and scene diversity, and developing broader real-world benchmarks to better assess model generalization.
Deep Analysis
Background
The development of marine perception technology is limited by data scarcity. Traditional real-world data collection is costly and time-consuming, especially in underwater environments where light attenuation and scattering complicate visual perception tasks. Synthetic data generation offers a new solution by simulating large-scale labeled data, reducing reliance on real data.
Core Problem
Data scarcity in underwater environments limits machine learning model training and application. Light attenuation and scattering pose significant challenges to underwater visual perception tasks, and existing datasets are insufficient for large-scale model training.
Innovation
The core innovation of this paper is the development of a synthetic data generation pipeline based on OceanSim, capable of generating large-scale, automatically labeled photorealistic datasets. The pipeline supports semantic annotations across multiple sensor modalities, enhancing simulator perceptual fidelity and scene diversity.
Methodology
- �� Extend OceanSim simulator with a synthetic data generation pipeline using IsaacSim.
- �� Generate diverse synthetic data by configuring scene appearance, structure, and sensor settings.
- �� Validate synthetic data effectiveness in a sea urchin detection task, analyzing the impact of different scene variations on performance.
Experiments
Experimental design includes training models with synthetic data and testing on real datasets. Two detectors, YOLO and DETR, are used to evaluate the impact of different scene variations and dataset sizes on model performance.
Results
Results show synthetic data effectively enhances model performance on real data, especially in complex backgrounds and with distractors. Increasing the synthetic dataset size did not significantly enhance performance, indicating diversity is more crucial than quantity.
Applications
The method can be used for underwater robotic perception tasks, such as object detection and semantic segmentation. Synthetic data generation reduces reliance on real data, advancing underwater perception technology.
Limitations & Outlook
The current light transport model simplifies underwater optical effects, failing to fully simulate real-world light transport characteristics. Scene diversity is limited by the number of underlying assets, affecting data breadth.
Plain Language Accessible to non-experts
Imagine cooking in a kitchen. You need various ingredients and tools to complete a dish. Synthetic data generation is like a virtual kitchen where you can simulate different ingredients and tools without actually going to the store. This way, you can quickly try different recipes and find the best method. Similarly, in underwater environments, synthetic data generation technology simulates various scenes and conditions to train and test machine learning models.
ELI14 Explained like you're 14
Imagine you're playing a virtual reality game with an underwater world. You can explore this world freely, seeing various marine life and environments. Synthetic data generation is like the game's production team, creating this virtual world so you can experience underwater adventures from home. This way, scientists can simulate ocean environments in the lab, training their robots to recognize and explore objects in the sea.
Glossary
Synthetic Data Generation
Data generated through computer simulation to replace or supplement real data.
Used to generate large-scale labeled datasets, reducing data collection costs.
OceanSim
An IsaacSim-based underwater perception simulator supporting synthetic data generation.
Used to generate realistic underwater scenes and data.
IsaacSim
A robotics simulation platform developed by NVIDIA, supporting high-fidelity physical simulation.
The foundational platform for OceanSim.
YOLO
A real-time object detection algorithm capable of quickly identifying objects in images.
Used to evaluate the effectiveness of synthetic data generation.
DETR
A transformer-based object detection algorithm with strong generalization capabilities.
Used to evaluate the effectiveness of synthetic data generation.
Open Questions Unanswered questions from this research
- 1 How to better simulate real underwater optical effects in synthetic data to narrow the sim-to-real gap.
- 2 How to increase scene diversity to improve model generalization.
Applications
Immediate Applications
Underwater Robotic Perception
Use synthetic data generation technology to train underwater robots for object detection and semantic segmentation, reducing reliance on real data.
Long-term Vision
Marine Environment Monitoring
Utilize synthetic data generation technology to develop more intelligent marine monitoring systems, improving efficiency in marine resource management and protection.
Abstract
Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data. This data is often expensive and laborious to gather, making large-scale real-world data challenging to gather and curate. However, simulated data can help close the gap, enabling many learning-based tasks for underwater perception. In this work, we extend OceanSim, an IsaacSim-based underwater perception simulator, with a Synthetic Data Generation (SDG) pipeline for training models to be used in underwater scenarios. The proposed pipeline enables users to generate large, automatically labeled, photorealistic datasets with configurable scene appearance, structure, and sensor settings. We evaluate the pipeline on a real-world sea urchin detection task and study how different forms of synthetic scene variation affect sim-to-real performance. Based on these experiments, we discuss findings on our results, main limitations of the current pipeline and identify future directions for improving underwater rendering fidelity, scene diversity, and the evaluation of sim-to-real generalization. The open-source code can be found at https://github.com/umfieldrobotics/OceanSim.