Physical Property Understanding from Language-Embedded Feature Fields

• URL: http://arxiv.org/abs/2404.04242v1
• Date: Fri, 5 Apr 2024 17:45:07 GMT
• Title: Physical Property Understanding from Language-Embedded Feature Fields
• Authors: Albert J. Zhai, Yuan Shen, Emily Y. Chen, Gloria X. Wang, Xinlei Wang, Sheng Wang, Kaiyu Guan, Shenlong Wang,
• Abstract summary: We present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision, we leverage large language models to propose candidate materials for each object. Our method is accurate, annotation-free, and applicable to any object in the open world.
• Score: 27.151380830258603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper, we present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision, we leverage large language models to propose candidate materials for each object. We then construct a language-embedded point cloud and estimate the physical properties of each 3D point using a zero-shot kernel regression approach. Our method is accurate, annotation-free, and applicable to any object in the open world. Experiments demonstrate the effectiveness of the proposed approach in various physical property reasoning tasks, such as estimating the mass of common objects, as well as other properties like friction and hardness.

Related papers

• Compositional Physical Reasoning of Objects and Events from Videos [122.6862357340911]
  This paper addresses the challenge of inferring hidden physical properties from objects' motion and interactions. We evaluate state-of-the-art video reasoning models on ComPhy and reveal their limited ability to capture these hidden properties. We also propose a novel neuro-symbolic framework, Physical Concept Reasoner (PCR), that learns and reasons about both visible and hidden physical properties.
  arXiv  Detail & Related papers  (2024-08-02T15:19:55Z)
• PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models [58.33913881592706]
  Humans can easily apply their intuitive physics to grasp skillfully and change grasps efficiently, even for objects they have never seen before. This work delves into infusing such physical commonsense reasoning into robotic manipulation. We introduce PhyGrasp, a multimodal large model that leverages inputs from two modalities: natural language and 3D point clouds.
  arXiv  Detail & Related papers  (2024-02-26T18:57:52Z)
• Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties [100.19685489335828]
  This work proposes a novel dataset and benchmark, termed Physion++, to rigorously evaluate visual physical prediction in artificial systems. We test scenarios where accurate prediction relies on estimates of properties such as mass, friction, elasticity, and deformability. We evaluate the performance of a number of state-of-the-art prediction models that span a variety of levels of learning vs. built-in knowledge, and compare that performance to a set of human predictions.
  arXiv  Detail & Related papers  (2023-06-27T17:59:33Z)
• Intrinsic Physical Concepts Discovery with Object-Centric Predictive Models [86.25460882547581]
  We introduce the PHYsical Concepts Inference NEtwork (PHYCINE), a system that infers physical concepts in different abstract levels without supervision. We show that object representations containing the discovered physical concepts variables could help achieve better performance in causal reasoning tasks.
  arXiv  Detail & Related papers  (2023-03-03T11:52:21Z)
• ComPhy: Compositional Physical Reasoning of Objects and Events from Videos [113.2646904729092]
  The compositionality between the visible and hidden properties poses unique challenges for AI models to reason from the physical world. Existing studies on video reasoning mainly focus on visually observable elements such as object appearance, movement, and contact interaction. We propose an oracle neural-symbolic framework named Compositional Physics Learner (CPL), combining visual perception, physical property learning, dynamic prediction, and symbolic execution.
  arXiv  Detail & Related papers  (2022-05-02T17:59:13Z)
• Visual Grounding of Learned Physical Models [66.04898704928517]
  Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. We present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.
  arXiv  Detail & Related papers  (2020-04-28T17:06:38Z)

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.