SceneNet: Understanding Real World Indoor Scenes With Synthetic Data (1511.07041v2)

Abstract: Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning have shown promise in this direction but also highlighted the need for enormous quantity of supervised data --- performance increases in proportion to the amount of data used. However, this quickly becomes prohibitive when considering the manual labour needed to collect such data. In this work, we focus our attention on depth based semantic per-pixel labelling as a scene understanding problem and show the potential of computer graphics to generate virtually unlimited labelled data from synthetic 3D scenes. By carefully synthesizing training data with appropriate noise models we show comparable performance to state-of-the-art RGBD systems on NYUv2 dataset despite using only depth data as input and set a benchmark on depth-based segmentation on SUN RGB-D dataset. Additionally, we offer a route to generating synthesized frame or video data, and understanding of different factors influencing performance gains.

• The paper introduces SceneNet, a framework for generating virtually unlimited synthetic labeled data to overcome real data scarcity in indoor scene understanding.
• It employs automated scene synthesis with noise model integration to produce high-quality per-pixel depth annotations simulating real-world sensors.
• Fine-tuning models trained on SceneNet with smaller real datasets significantly boosts segmentation accuracy on benchmarks like NYUv2 and SUN RGB-D.

Overview of SceneNet: Understanding Real World Indoor Scenes With Synthetic Data

This paper, entitled "SceneNet: Understanding Real World Indoor Scenes With Synthetic Data," addresses the challenges of scene understanding in indoor environments through a novel approach that utilizes synthetic data. The authors highlight the significant demand for large labeled datasets required for training deep learning models in semantic scene understanding, specifically focusing on per-pixel depth-based semantic labeling.

The paper begins by establishing the importance of scene understanding for various high-level automated tasks. These include, but are not limited to, robotic navigation, item arrangement, and 3D modeling. Traditional supervised learning methods, while promising, are limited by the scarcity of sufficiently large and labeled datasets like NYUv2 and SUN RGB-D.

Approach and Contributions

The central contribution of the paper is the development of SceneNet, a repository of synthetic 3D indoor scenes from which virtually unlimited labeled training data can be generated. This is achieved through the following key elements:

• Synthetic Data Generation: They create a sizable library of synthetic scenes, each annotated, enabling the generation of high-quality per-pixel labeled data.
• Automated Scene Synthesis: Leveraging computer graphics and automated methods like simulated annealing, the paper presents a mechanism to model realistic scenes using object repositories like ModelNet and ShapeNet.
• Noise Model Integration: To ensure that synthetic data resembles real-world depth data, noise models simulate realistic data imperfections seen in actual depth sensors.
• SceneNet Dataset: SceneNet encompasses a collection of basis scenes across various categories (bedrooms, living rooms, etc.), offering flexibility in generating diverse training samples.

Numerical Results and Comparisons

The authors present an empirical analysis demonstrating the effectiveness of their method by training deep learning models. They show that models trained on synthetic data from SceneNet, and further fine-tuned on a smaller real-world dataset, achieve competitive semantic segmentation results on the NYUv2 and SUN RGB-D datasets. Notably:

• A significant increase in class and global accuracy is observed when models trained on synthetic data are fine-tuned on real data, compared to models trained solely on real datasets.
• The segmentation method using SceneNet-FT-NYU-DHA achieves competitive accuracy, particularly for geometrically delineated items, comparable to state-of-the-art multi-modal systems using RGB, depth, and normals.

Practical and Theoretical Implications

The methodology has distinct implications:

• Practical: Synthetic datasets like SceneNet could alleviate the bottleneck of training data scarcity in scene understanding tasks, enabling the broader application of AI in robotics and autonomous navigation systems.
• Theoretical: The approach reaffirms the viability of synthetic data for training robust deep learning models, suggesting that even single-modality systems can effectively approximate the performance of more complex setups.

Future Directions

The research outlined in the paper opens avenues for further exploration:

1. Extending this approach to dynamic scenes and time-sequential data to improve RGB-D video understanding.
2. Large-scale synthesis and application of deep reinforcement learning for interactive scene understanding.
3. Continuing to refine and democratize the use of synthetic datasets for other AI applications.

This paper makes a substantive contribution to the field of computer vision by demonstrating that synthetic data can be a powerful tool in overcoming the limitations of data availability for indoor scene understanding, paving a path for future research in AI and machine learning.