FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction

• URL: http://arxiv.org/abs/2410.08282v1
• Date: Thu, 10 Oct 2024 18:07:07 GMT
• Title: FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction
• Authors: Irving Fang, Kairui Shi, Xujin He, Siqi Tan, Yifan Wang, Hanwen Zhao, Hung-Jui Huang, Wenzhen Yuan, Chen Feng, Jing Zhang,
• Abstract summary: Humans effortlessly integrate common-sense knowledge with sensory input from vision and touch to understand their surroundings. We introduce FusionSense, a novel 3D reconstruction framework that enables robots to fuse priors from foundation models with highly sparse observations from vision and tactile sensors.
• Score: 17.367277970910813
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Humans effortlessly integrate common-sense knowledge with sensory input from vision and touch to understand their surroundings. Emulating this capability, we introduce FusionSense, a novel 3D reconstruction framework that enables robots to fuse priors from foundation models with highly sparse observations from vision and tactile sensors. FusionSense addresses three key challenges: (i) How can robots efficiently acquire robust global shape information about the surrounding scene and objects? (ii) How can robots strategically select touch points on the object using geometric and common-sense priors? (iii) How can partial observations such as tactile signals improve the overall representation of the object? Our framework employs 3D Gaussian Splatting as a core representation and incorporates a hierarchical optimization strategy involving global structure construction, object visual hull pruning and local geometric constraints. This advancement results in fast and robust perception in environments with traditionally challenging objects that are transparent, reflective, or dark, enabling more downstream manipulation or navigation tasks. Experiments on real-world data suggest that our framework outperforms previously state-of-the-art sparse-view methods. All code and data are open-sourced on the project website.

Related papers

• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection [10.162882793554191]
  3D articulated object manipulation is essential for enabling robots to interact with their environment. Many existing studies make use of 3D point clouds as the primary input for manipulation policies. RGB images offer high-resolution observations using cost effective devices but lack spatial 3D geometric information. This framework is designed to capture multiple perspectives of the target object and infer depth information to complement its geometry.
  arXiv  Detail & Related papers  (2023-10-13T12:42:54Z)
• Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization [13.473742114288616]
  We propose a framework that can autonomously detect and localize objects in a known environment. The framework consists of three key elements: understanding the environment through RGB data, estimating depth through multi-modal sensor fusion, and managing artifacts. Experiments show that the proposed framework can accurately detect 98% of the objects in the real sample environment, without post-processing.
  arXiv  Detail & Related papers  (2023-07-03T15:51:39Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• 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)
• Active 3D Shape Reconstruction from Vision and Touch [66.08432412497443]
  Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. In 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings. We introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile priors to guide the shape exploration; and 3) a set of data-driven solutions with either tactile or visuo
  arXiv  Detail & Related papers  (2021-07-20T15:56:52Z)
• 3D Shape Reconstruction from Vision and Touch [62.59044232597045]
  In 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. We introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects.
  arXiv  Detail & Related papers  (2020-07-07T20:20:33Z)

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.