ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations
ObjectFolder integrates visual, auditory, and tactile implicit neural representations for multisensory object modeling, enabling recognition, retrieval, 3D reconstruction, and robotic grasping.
Key Findings
Methodology
This work introduces ObjectFolder, a multisensory dataset encoding visual, auditory, and tactile data via neural implicit functions (VisionNet, AudioNet, TouchNet). Objects are modeled through physics-based simulations and modal analysis, generating high-fidelity sensory data. The neural networks encode these modalities into compact representations, enabling multi-task applications. Experiments demonstrate superior performance in instance recognition (accuracy >99%), cross-modal retrieval (mAP >0.9), and 3D shape reconstruction (IoU ~0.87). The dataset supports robotic tasks like grasping, validated through simulation and real-world transfer. The approach leverages neural radiance fields for visual encoding, modal analysis for sound synthesis, and ray-tracing for tactile simulation, forming a unified, efficient framework for multisensory object understanding.
Key Results
- Single-modality recognition accuracy: vision 94.8%, audio 98.3%, touch 72.4%. Multimodal fusion boosts accuracy to 99.8%.
- Cross-modal retrieval achieves mAP >0.9, outperforming CCA-based methods; demonstrates effective shared embedding space.
- 3D reconstruction with AUDIO2MESH yields IoU 0.8729, surpassing visual-only models; combining audio and vision enhances geometric inference.
- Robotic grasping experiments show improved success rates when integrating visual and tactile data, confirming the benefit of multisensory cues.
Significance
This research advances multisensory perception by providing a comprehensive dataset and neural encoding framework, addressing limitations of existing geometric or single-modality datasets. It enables robots and AI systems to perceive objects more holistically, improving robustness and generalization in real-world tasks. The implicit neural representations facilitate scalable storage and flexible inference, paving the way for more intelligent autonomous agents capable of complex interactions in dynamic environments. The dataset's multi-task validation underscores its potential to unify perception, recognition, and manipulation, fostering breakthroughs across robotics, AR/VR, and embodied AI.
Technical Contribution
The paper introduces a novel multi-modal implicit neural representation framework, combining vision, sound, and touch into a unified model. It innovates by integrating physics-based modal analysis for sound synthesis with neural radiance fields for visual encoding, and ray-tracing for tactile simulation. The neural networks are trained jointly to produce compact, generalizable representations, enabling multi-task performance. The approach significantly reduces storage costs compared to raw data, while maintaining high fidelity. It also demonstrates effective cross-modal retrieval and 3D reconstruction, setting new benchmarks for multisensory object understanding.
Novelty
This is the first comprehensive dataset combining high-quality visual, auditory, and tactile data encoded via implicit neural networks. Unlike prior works limited to geometry or single modalities, ObjectFolder emphasizes full multisensory integration, enabling complex perception and control tasks. The use of physics-based modal analysis for sound and neural radiance fields for visual data, coupled with tactile simulation, represents a significant leap forward in creating scalable, versatile multisensory models. This work bridges the gap between physical object properties and neural representations, opening new research avenues.
Limitations
- The physics-based simulations, while realistic, may not fully capture real-world sensor noise and environmental variability, affecting transferability.
- The dataset size, though diverse, is limited to 100 objects, which may restrict generalization to more complex or dynamic scenes.
- Computational costs for training and inference remain high, especially for real-time applications, necessitating further optimization.
Future Work
Future directions include expanding the dataset with more objects and dynamic scenes, integrating real sensor data for domain adaptation, and developing real-time inference methods. Exploring self-supervised learning to reduce annotation efforts and enhancing multimodal fusion strategies for robustness are also promising. Additionally, applying the framework to real-world robotic systems and extending to other sensory modalities like smell or proprioception could further enrich multisensory AI capabilities.
AI Executive Summary
In recent years, multisensory perception has become a crucial frontier in robotics and AI, aiming to emulate human-like understanding of objects through multiple senses. Traditional datasets and models often focus on geometry or visual appearance alone, limiting the ability of machines to interpret complex real-world interactions. Recognizing this gap, the authors present ObjectFolder, a novel dataset that encodes objects through implicit neural representations across visual, auditory, and tactile modalities. This approach leverages physics-based simulations, modal analysis, and neural networks to generate high-fidelity, compact multisensory data, enabling a wide range of tasks from recognition to manipulation.
The core innovation lies in representing each object as an implicit neural network, which can be queried with extrinsic parameters to produce multi-view images, impact sounds, and tactile readings. This unified representation not only reduces storage requirements but also facilitates generalization across viewpoints and conditions. Extensive experiments demonstrate that models trained on ObjectFolder outperform existing methods in instance recognition, cross-modal retrieval, and 3D shape reconstruction, with accuracy exceeding 99% in recognition and IoU around 0.87 in reconstruction. The dataset’s versatility is further validated through robotic grasping tasks, where multisensory cues significantly improve success rates.
This work marks a significant step toward holistic object understanding, bridging the gap between physical properties and neural encoding. It offers a scalable, efficient platform for advancing multisensory AI, with broad implications for robotics, virtual reality, and embodied intelligence. Despite current limitations in environmental variability and dataset scale, the authors outline promising directions for future research, including real-world sensor integration and dynamic scene modeling. Overall, ObjectFolder sets a new standard for multisensory object datasets, fostering deeper integration of perception and control in autonomous systems.
Deep Analysis
Background
多模态感知是人类认知的基础，近年来在视觉、听觉和触觉等领域取得显著进展。Neural Radiance Fields（NeRF）等技术推动了高质量3D重建，但缺乏系统整合多模态信息的公共数据集。现有数据如YCB主要关注几何和视觉，缺少声音和触觉信息，限制了多模态感知的深度发展。虚拟化多感官数据成为解决方案，但真实感官数据采集难度大、成本高。隐式神经表示技术的出现，为高效存储和泛化多模态信息提供了可能，推动了多模态感知的理论与应用创新。
Core Problem
现有数据集多局限于单一模态或几何信息，缺乏高质量、多感官融合的统一平台。真实对象采集复杂、成本高，难以满足多任务、多场景的需求。如何在虚拟环境中模拟逼真的视觉、听觉和触觉信息，构建高效、可扩展的多模态数据集，成为制约多模态感知技术的瓶颈。同时，缺乏统一的编码机制，难以实现多模态信息的高效融合与推理。
Innovation
本研究的创新点包括：1）构建ObjectFolder多模态数据集，集成视觉、听觉、触觉信息，突破单一模态限制；2）采用隐式神经网络（Neural Radiance Fields）编码多感官信息，实现高效存储和泛化；3）结合物理模拟和模态分析，生成逼真、多样化的感官数据，支持多任务学习。这些创新极大丰富了多模态感知资源，推动了机器人自主感知与操作技术的发展。
Methodology
- �� 数据采集：从线上资源获取100个高质量3D对象，标注材质类型。• 视觉模拟：利用Blender渲染不同视角和光照条件下的图像，训练VisionNet编码外观特征。• 声音模拟：通过模态分析获得振动模态，利用物理模型模拟碰撞声，训练AudioNet编码声学信息。• 触觉模拟：使用TACTO模拟接触传感器，采集表面局部触觉图像，训练TouchNet编码触觉特征。• 神经编码：设计三支网络，将多模态信息编码为隐式神经表示，实现多视角、多模态的对象描述。• 任务应用：在实例识别、跨模态检索、3D重建和机器人抓取中验证模型性能。
Experiments
采用ObjectFolder中的数据进行多任务评估，比较单模态与多模态模型性能。指标包括识别准确率、平均精度（mAP）、IoU、Chamfer距离等。设置不同模态组合，分析融合效果。使用交叉验证和消融实验验证模型的鲁棒性与泛化能力。还在真实图片上测试模型的迁移能力，评估模拟逼真度。
Results
多模态融合模型在实例识别中达99.8%的准确率，明显优于单一模态。跨模态检索中，平均精度超过0.9，优于传统方法。3D重建中，结合音频信息的AUDIO2MESH模型IoU达0.8729，优于仅视觉模型。在机器人抓取任务中，融合视觉与触觉的模型显著提升成功率，验证多模态信息的协同作用。这些结果证明了多模态数据集的实用性和模型的有效性。
Applications
该数据集和模型可广泛应用于机器人自主感知、虚拟现实、增强现实等领域。机器人可以利用多感官信息实现更精准的环境理解与操作，提升自主性和鲁棒性。虚拟环境中，支持更真实的交互体验和场景重建。未来还可结合实际传感器，推动多模态感知系统的商业化与普及。
Limitations & Outlook
模型在复杂动态环境中的鲁棒性仍需提升，模拟的感官数据在实际硬件上可能存在偏差。数据集规模有限，未来需扩展多样性和复杂性。高计算成本限制了大规模训练，需优化算法和硬件资源。此外，当前多模态融合策略仍有提升空间，未来应探索更高效的融合机制。
Plain Language Accessible to non-experts
想象你在厨房做饭，手里拿着锅铲，能看到锅里的菜，听到锅里的声音，还能感觉到菜的温度和质地。这就像我们用多种感官同时感知一个物体：视觉告诉我们它的颜色和形状，听觉让我们知道它是否在响，触觉让我们感受到它的质感。科学家们也在用类似的方法，让机器人通过模拟这些感官，理解和操作各种物体。这个研究就像给机器人装上了“多感官感知器”，让它更聪明、更像人类一样感知世界。通过虚拟的“厨房”，他们让机器人学习识别不同的餐具、判断它们是否安全、甚至用它们完成任务。这种多模态感知技术，未来可以让机器人在复杂环境中更好地工作，比如自动仓库、智能家居，甚至太空探索。它就像给机器人装上了“眼睛、耳朵和手”，让它变得更聪明、更灵活。
ELI14 Explained like you're 14
想象你在玩一个超级酷的游戏，你的角色可以用眼睛看东西，用耳朵听声音，还能用手感觉到物体的温度和硬度。现在，科学家们也在让机器人拥有这些能力！他们用一种叫做ObjectFolder的特殊“数据包”，里面装满了机器人可以用来学习的各种“感官信息”。比如，机器人可以看到不同的东西，听到它们碰撞的声音，还能用“手”感觉到它们的表面。这就像你用眼睛、耳朵和手同时认识一个新玩具一样。科学家用复杂的数学模型，把这些感官信息变成一种“神经网络”，让机器人可以理解和区分不同的物体。这样，机器人就能更聪明地完成任务，比如抓取、识别、甚至在虚拟世界中重建物体的3D模型。这项技术未来可以让机器人变得更像人类，能在复杂的环境中自如行动，就像你在游戏中用多种感官探索世界一样！
Abstract
Multisensory object-centric perception, reasoning, and interaction have been a key research topic in recent years. However, the progress in these directions is limited by the small set of objects available -- synthetic objects are not realistic enough and are mostly centered around geometry, while real object datasets such as YCB are often practically challenging and unstable to acquire due to international shipping, inventory, and financial cost. We present ObjectFolder, a dataset of 100 virtualized objects that addresses both challenges with two key innovations. First, ObjectFolder encodes the visual, auditory, and tactile sensory data for all objects, enabling a number of multisensory object recognition tasks, beyond existing datasets that focus purely on object geometry. Second, ObjectFolder employs a uniform, object-centric, and implicit representation for each object's visual textures, acoustic simulations, and tactile readings, making the dataset flexible to use and easy to share. We demonstrate the usefulness of our dataset as a testbed for multisensory perception and control by evaluating it on a variety of benchmark tasks, including instance recognition, cross-sensory retrieval, 3D reconstruction, and robotic grasping.