ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

• URL: http://arxiv.org/abs/2204.02389v1
• Date: Tue, 5 Apr 2022 17:55:01 GMT
• Title: ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer
• Authors: Ruohan Gao, Zilin Si, Yen-Yu Chang, Samuel Clarke, Jeannette Bohg, Li Fei-Fei, Wenzhen Yuan, Jiajun Wu
• Abstract summary: We present Object 2.0, a large-scale dataset of common household objects in the form of implicit neural representations. Our dataset is 10 times larger in the amount of objects and orders of magnitude faster in time. We show that models learned from virtual objects in our dataset successfully transfer to their real-world counterparts.
• Score: 46.24535144252644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects [51.22194706674366]
  We introduce the Object Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning. We also introduce the Object Real dataset, including the multisensory measurements for 100 real-world household objects.
  arXiv  Detail & Related papers  (2023-06-01T17:51:22Z)
• OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation [107.71752592196138]
  We propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets. Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos.
  arXiv  Detail & Related papers  (2023-01-18T18:14:18Z)
• Object Scene Representation Transformer [56.40544849442227]
  We introduce Object Scene Representation Transformer (OSRT), a 3D-centric model in which individual object representations naturally emerge through novel view synthesis. OSRT scales to significantly more complex scenes with larger diversity of objects and backgrounds than existing methods. It is multiple orders of magnitude faster at compositional rendering thanks to its light field parametrization and the novel Slot Mixer decoder.
  arXiv  Detail & Related papers  (2022-06-14T15:40:47Z)
• ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations [52.226947570070784]
  We present Object, a dataset of 100 objects that addresses both challenges with two key innovations. First, Object encodes the visual, auditory, and tactile sensory data for all objects, enabling a number of multisensory object recognition tasks. Second, Object employs a uniform, object-centric simulations, and implicit representation for each object's visual textures, tactile readings, and tactile readings, making the dataset flexible to use and easy to share.
  arXiv  Detail & Related papers  (2021-09-16T14:00:59Z)
• Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction [7.013794773659423]
  Common Objects in 3D is a large-scale dataset with real multi-view images of object categories annotated with camera poses and ground truth 3D point clouds. The dataset contains a total of 1.5 million frames from nearly 19,000 videos capturing objects from 50 MS-COCO categories. We exploit this new dataset to conduct one of the first large-scale "in-the-wild" evaluations of several new-view-synthesis and category-centric 3D reconstruction methods.
  arXiv  Detail & Related papers  (2021-09-01T17:59:05Z)
• REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter [52.117388513480435]
  We present a new dataset named regrad to sustain the modeling of relationships among objects and grasps. Our dataset is collected in both forms of 2D images and 3D point clouds. Users are free to import their own object models for the generation of as many data as they want.
  arXiv  Detail & Related papers  (2021-04-29T05:31:21Z)
• ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition [21.594641488685376]
  We present the ORBIT dataset and benchmark, grounded in a real-world application of teachable object recognizers for people who are blind/low vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions.
  arXiv  Detail & Related papers  (2021-04-08T15:32:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.