Synthesizing Training Data for Object Detection in Indoor Scenes (1702.07836v2)

Abstract: Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.

Synthesizing Training Data for Object Detection in Indoor Scenes

The paper, "Synthesizing Training Data for Object Detection in Indoor Scenes" by Georgios Georgakis et al., explores an innovative approach to training object detectors using synthetically generated training data. The authors address a critical challenge in object detection within cluttered indoor environments, where obtaining large amounts of annotated training data is both time-consuming and costly. This research presents methodologies to leverage synthetic data, particularly focusing on enhancing Convolutional Neural Networks (CNNs) employed for object detection.

Context and Motivation

Object detection in indoor scenes is a pivotal capability for service robots executing tasks such as search and retrieval in complex environments. Traditional object detection techniques usually require extensive manually labeled datasets to achieve satisfactory performance. However, these datasets are cumbersome to create and maintain, prompting the exploration for alternative strategies. This work is notable for integrating synthetically generated composite images with real-world scenes, assessing their utility in training sophisticated object detection systems.

Key Methodological Insights

The core methodological approach involves superimposing 2D images of textured object models onto real scenes, utilizing several superimposition strategies. These range from basic image-based blending to more advanced methods informed by depth and semantics. The paper emphasizes creating synthetic images by precisely placing objects in backdrop images that simulate realistic conditions, exploiting existing object model repositories.

The authors employ CNN-based object detectors, namely Faster R-CNN and Single-Shot Multibox Detector (SSD), to evaluate their training protocols' effectiveness. They critically investigate how synthetic data impacts model training and explore various blending and scaling techniques to mitigate any synthetic-to-real domain discrepancies.

Experimental Evaluation

The research meticulously evaluates various combinations of generation parameters across two datasets: GMU-Kitchens and Washington RGB-D Scenes v2. Results demonstrate that utilizing synthetic data augmented with a fraction of real data can achieve detection accuracies comparable to using an entirely real dataset. This underscores the potential to significantly reduce the annotation burden in data-driven learning settings.

A notable finding is that when the detectors are trained with synthetic data corroborated by only 10% of real training images, accuracies surpass those achieved using purely real data. Importantly, informed superimposition strategies notably bolster model performance in contrast to randomized placements, reflecting the importance of contextual realism in synthetic data generation.

Implications and Future Directions

This paper suggests promising avenues for reducing the dependency on large annotated datasets for object detection applications. Its implications are particularly significant in scenarios where obtaining diverse, high-quality annotations is restrictive. The enhanced understanding of synthetic data utility can pave the way for efficient applications in robotics and autonomous systems, potentially extending its applicability in dynamic or previously unseen environments.

Theoretical implications encourage further research into methods for domain adaptation and transfer learning that might bridge the gap between synthetic and real data domains more effectively. Practically, robotic systems in dynamic environments stand to benefit from reduced data annotation requirements and improved adaptability to novel environments.

Conclusion

In conclusion, the authors chart new pathways in object detection by demonstrating the effectiveness of synthesizing training data for CNN-based detectors in indoor environments. This research lays foundational work for subsequent explorations in synthetic data utilization, presenting a robust framework that integrates geometric and semantic scene understanding techniques to refine synthetic image generation. It marks a strategic step forward in balancing synthetic and real data for enhanced performance in robotic indoor scene understanding and manipulation.