ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer (2204.02389v1)

Abstract: Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent dataset that introduces 100 virtualized objects with visual, acoustic, and tactile sensory data. However, the dataset is small in scale and the multisensory data is of limited quality, hampering generalization to real-world scenarios. We present ObjectFolder 2.0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances ObjectFolder 1.0 in three aspects. First, our dataset is 10 times larger in the amount of objects and orders of magnitude faster in rendering time. Second, we significantly improve the multisensory rendering quality for all three modalities. Third, we show that models learned from virtual objects in our dataset successfully transfer to their real-world counterparts in three challenging tasks: object scale estimation, contact localization, and shape reconstruction. ObjectFolder 2.0 offers a new path and testbed for multisensory learning in computer vision and robotics. The dataset is available at https://github.com/rhgao/ObjectFolder.

• The paper introduces a groundbreaking 1,000-object multisensory dataset that significantly enhances Sim2Real transfer for practical robotics applications.
• It utilizes advanced implicit neural representations to deliver high-fidelity visual, audio, and tactile simulations with drastically reduced rendering times.
• Results demonstrate robust transfer to real-world tasks, including object scale estimation, contact localization, and shape reconstruction.

Multisensory Learning and Sim2Real Transfer with ObjectFolder

The paper "ObjectFolder: A Multisensory Object Dataset for Sim2Real Transfer" presents a comprehensive enhancement over previous efforts in constructing datasets tailored to multisensory learning. The authors address the limitations of prior work, particularly the lack of realism and inadequate multisensory data, by introducing a significantly more extensive and robust dataset: ObjectFolder. This dataset provides high-fidelity multisensory data across 1,000 virtual objects, utilizing implicit neural representations to encode visual, acoustic, and tactile information, thereby facilitating effective Sim2Real transfer in computer vision and robotics.

Key Contributions

The paper highlights three main contributions which significantly advance the state of multisensory learning datasets:

1. Dataset Scale and Efficiency: The authors expanded the object set by an order of magnitude, offering 1,000 virtual objects compared to previous datasets. This scale assumes critical importance for generalizability in real-world applications. The rendering times have been decreased by orders of magnitude, making real-time multisensory interaction practical.
2. Improved Quality of Multisensory Data: The paper presents significant improvements in the fidelity of visual, auditory, and tactile renderings. These enhancements are supported by advanced simulation techniques and the use of sophisticated neural architectures, ensuring a higher quality of sensory simulation that better approximates real-world conditions.
3. Successful Sim2Real Transfer: ObjectFolder achieves a notable transfer of learned models from virtual environments to real-world tasks. The effectiveness of this transfer is illustrated through three specific tasks: object scale estimation, contact localization, and shape reconstruction. The results indicate that learning from ObjectFolder's virtual objects can be effectively applied to real-world counterparts, underscoring the utility of the dataset for practical robotics and computer vision applications.

Technical Improvements

The authors employ implicit neural representations to efficiently encapsulate multisensory data. Each rendered object uses sub-network architectures (VisionNet, AudioNet, TouchNet) as components of the Object File representation:

• Vision: Utilizing KiloNeRF strategies, the representation of visual data is made both faster and more photorealistic.
• Audio: Through refined FEM-based modal analysis, auditory data is achieved with higher accuracy and realism. The authors mitigate the complexity by predicting mode-specific gains, allowing for dynamic reconstruction of audio signals.
• Touch: Enhanced tactile representation allows for varied angles and depths of contact, significantly broadening the contextual touch-based learning applications.

Implications and Future Directions

The advancements presented could have a profound impact on the field of robotics, as they provide a robust testbed for multisensory learning. The potential applications are diverse, ranging from improving object recognition in complex environments to enabling nuanced robotic interactions through enhanced sensory understanding.

Future work could extend beyond rigid, homogeneous objects to incorporate the complexity of composite materials and dynamic environments. Additionally, exploring contextually adaptive models that can leverage environmental variables like lighting or acoustic conditions would further enhance Sim2Real transfer capabilities.

Overall, the ObjectFolder dataset marks a pivotal development in multisensory learning, offering a substantial foundation for advancing the fidelity and applicability of robotic perception systems in varied practical and theoretical domains.