Kubric: A scalable dataset generator (2203.03570v1)

Abstract: Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.

• The paper introduces Kubric, an open-source framework that efficiently generates large-scale, richly-annotated synthetic datasets for vision tasks.
• The paper details a novel pipeline integrating Blender and PyBullet to produce photorealistic scenes and accurate annotations at scale.
• The paper demonstrates Kubric's impact by enhancing model performance in optical flow, object segmentation, and pose estimation through comprehensive experiments.

Kubric: A Scalable Dataset Generator

The paper presents "Kubric," an open-source Python framework designed to facilitate the generation of high-quality synthetic datasets with rich annotations. The work addresses critical challenges in data curation for machine learning, particularly within the field of vision tasks, by leveraging the potential of synthetic data. Through seamless integration with tools like PyBullet and Blender, Kubric endeavors to offer a comprehensive and scalable solution that generates photorealistic scenes while maintaining detailed annotations. Here, we provide an analytical overview of the contributions, methodology, experiments, and implications of this framework in machine learning and artificial intelligence.

Data Synthesis and Its Importance

The strength of many machine learning models hinges significantly on the quality and quantity of training data available. Obtaining annotated real-world data at scale is often prohibitively expensive and faces hurdles related to privacy, fairness, and potential legal issues. Synthetic data offers a viable alternative, though existing tools for generating such data lag behind in maturity when compared to tools for model architecture design.

Kubric tackles these issues head-on by providing a pipeline that not only generates realistic image and video data but also scales effortlessly to accommodate large computational jobs, generating terabytes of data. In doing so, Kubric aims to bridge the gap between architecture and data generation, providing a holistic platform for researchers.

Framework Design and Characteristics

The design of Kubric centers around several guiding principles:

• Openness: The framework and its datasets are open-source, promoting accessibility and reproducibility within the research community.
• Ease of Use: With a simple Python API interface, Kubric streamlines the data generation process, abstracting the complexities of managing different rendering engines and simulators.
• Realism: By utilizing the Cycles ray tracing engine of Blender, Kubric supports advanced optical effects necessary for realistic datasets.
• Scalability: The framework can manage workloads ranging from local testing to large-scale data generation across thousands of machines in a cloud environment.
• Portability and Reproducibility: By providing Docker containers, Kubric ensures high portability and eases the replication of datasets across diverse computational environments.
• Rich Annotations: The pipeline supports an extensive range of annotations such as optical flow, segmentation, and depth maps, integral for diverse vision tasks.

Contributions and Experiments

Kubric's versatility is demonstrated through its application in generating various datasets tailored to specific vision challenges:

1. Object Discovery: Facilitating object instance segmentation in complex scenes using video data was shown to support new approaches for temporally-consistent segmentation masks over time.
2. Optical Flow: By enabling 3D rigid-body motion in its dataset, Kubric overcomes limitations faced by older 2D datasets, as highlighted in the notable improvements in pre-training efficacy for tasks like optical flow.
3. Texture-Structure Analysis in NeRF: Kubric allows the exploration of how surface reconstruction accuracy in Neural Radiance Fields (NeRFs) varies with texture frequency, providing insights into model limitations.
4. Pose Estimation and Transfer Learning: Demonstrating improvements in human pose estimation across diverse datasets and tasks, Kubric underscores the potential of synthetic data to enhance model robustness and generalization.

Implications and Future Work

Kubric represents a significant step forward in the creation of large-scale, richly-annotated synthetic datasets. Its open-source nature not only democratizes access to high-quality data for model training but also encourages the adoption of synthetic data across machine learning pipelines where privacy and scalability are paramount concerns. The framework provides a versatile and scalable platform capable of adapting to diverse research needs, fostering innovation in data creation.

Looking ahead, further extensions and augmentation of the framework could lead to the broader adoption of such approaches in areas demanding sophisticated AI solutions. Future work would likely involve enhancing compatibility with other rendering engines, expanding asset libraries, and refining annotations to cater to evolving machine learning challenges.

Kubric's contribution to the field marks a significant milestone in synthetic data generation, potentially steering the community towards methodologies that better harmonize data quality and computational efficiency while mitigating societal biases. Its adoption could catalyze new frontiers in AI development and research.