Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2011.02523v5)

Abstract: For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding. To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Our dataset: (1) relies exclusively on publicly available 3D assets; (2) includes complete scene geometry, material information, and lighting information for every scene; (3) includes dense per-pixel semantic instance segmentations and complete camera information for every image; and (4) factors every image into diffuse reflectance, diffuse illumination, and a non-diffuse residual term that captures view-dependent lighting effects. We analyze our dataset at the level of scenes, objects, and pixels, and we analyze costs in terms of money, computation time, and annotation effort. Remarkably, we find that it is possible to generate our entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model. We also evaluate sim-to-real transfer performance on two real-world scene understanding tasks - semantic segmentation and 3D shape prediction - where we find that pre-training on our dataset significantly improves performance on both tasks, and achieves state-of-the-art performance on the most challenging Pix3D test set. All of our rendered image data, as well as all the code we used to generate our dataset and perform our experiments, is available online.

Hypersim: Insights into a Synthetic Dataset for Indoor Scene Understanding

The research paper "Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding" addresses a significant challenge in scene understanding tasks: obtaining per-pixel ground truth labels from real-world images. The authors introduce Hypersim, a synthetic dataset designed to facilitate research in indoor scene understanding by providing detailed photorealistic synthetic images with comprehensive labeling and geometric information.

Key Contributions

The dataset, Hypersim, stands out due to several critical features:

1. High Fidelity Data: Comprising 77,400 images across 461 indoor scenes, Hypersim offers dense per-pixel semantic and instance segmentations, alongside detailed lighting and material information. This level of granularity is instrumental for various tasks, including geometric learning and inverse rendering.
2. Cost-Effective Generation: The dataset was generated using a proprietary computational pipeline that is cost-efficient, costing approximately half of what it would take to train a standard open-source NLP model. This aspect highlights its practicality for large-scale synthetic data generation initiatives.
3. Sim-to-Real Transfer: One of the vital evaluations undertaken was the sim-to-real transfer performance. Particularly in tasks such as semantic segmentation and 3D shape prediction, using Hypersim for pre-training significantly enhanced performance metrics when compared to state-of-the-art real-world datasets. Notable successes included achieving state-of-the-art results on challenging benchmarks like Pix3D.

Methodological Insights

The authors implemented a novel viewpoint sampling strategy to accentuate salient objects within each scene, deemed beneficial for synthesizing informative and realistic training data. Additionally, an intelligent mesh annotation tool was devised to streamline the process of semantic labeling, thus amplifying the dataset's utility.

Experimental Results

The paper presented comprehensive evaluations across various tasks:

• For semantic segmentation, models pre-trained on Hypersim demonstrated improved mIoU scores on the NYUv2 dataset, indicating superior generalization capabilities when real-world labeled data is scarce.
• In the field of 3D shape prediction, pre-training with Hypersim provided a notable performance boost, setting new benchmarks for AP metrics on Pix3D's $\mathcal{S}_2$ test set.

These empirical insights underscore the dataset's efficacy in enhancing neural network performance across multiple domains.

Broader Implications and Future Directions

Hypersim's introduction signifies a meaningful leap in the accessibility of sophisticated, annotated synthetic datasets for indoor environments. In fields such as robotics, augmented reality, and virtual environment modeling, where real-world data acquisition is labor-intensive and costly, Hypersim provides a promising alternative.

Moreover, the success of Hypersim opens avenues for further exploration into hybrid synthetic-real training regimes and the development of more refined photorealistic rendering techniques. Potential areas for future research include investigating optimal configurations of synthetic and real data in training models and enhancing the dataset's scales or diversities by incorporating interactive or dynamic environmental elements.

In summary, Hypersim represents a significant contribution to the toolkit available to researchers in computer vision, particularly those focused on indoor scene understanding. Its well-structured methodology and demonstrated application to real-world challenges make it a valuable asset, likely to influence future work in synthetic data curation and application.