Visual Looming from Motion Field and Surface Normals

• URL: http://arxiv.org/abs/2210.04108v1
• Date: Sat, 8 Oct 2022 21:36:49 GMT
• Title: Visual Looming from Motion Field and Surface Normals
• Authors: Juan Yepes and Daniel Raviv
• Abstract summary: Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. We derive novel solutions for obtaining visual looming quantitatively from the 2D motion field resulting from a six-degree-of-freedom motion of an observer relative to a local surface in 3D. We present novel methods to estimate visual looming from spatial derivatives of optical flow without the need for knowing range.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. In this paper we derive novel solutions for obtaining visual looming quantitatively from the 2D motion field resulting from a six-degree-of-freedom motion of an observer relative to a local surface in 3D. We also show the relationship between visual looming and surface normals. We present novel methods to estimate visual looming from spatial derivatives of optical flow without the need for knowing range. Simulation results show that estimations of looming are very close to ground truth looming under some assumptions of surface orientations. In addition, we present results of visual looming using real data from the KITTI dataset. Advantages and limitations of the methods are discussed as well.

Related papers

• PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes [7.736445799116692]
  We propose a novel method for monocular depth estimation in dynamic scenes. We first explore the arbitrariness of object's movement trajectory in dynamic scenes theoretically. To overcome the depth inconsistency problem around the edges, we propose a deformable support window module.
  arXiv  Detail & Related papers  (2024-11-04T03:42:29Z)
• GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation [60.33467489955188]
  This paper studies the problem of estimating physical properties (system identification) through visual observations. To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework. We propose a new dynamic 3D Gaussian framework based on motion factorization to recover the object as 3D Gaussian point sets. In addition to the extracted object surfaces, the Gaussian-informed continuum also enables the rendering of object masks during simulations.
  arXiv  Detail & Related papers  (2024-06-21T07:37:17Z)
• Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations [0.0]
  We describe a new, unbiased, and machine learning based approach to obtain useful scientific insights from a broad range of simulations. Our concept is based on applying nonlinear dimensionality reduction to learn compact representations of the data in a low-dimensional space. We present a prototype using a rotational invariant hyperspherical variational convolutional autoencoder, utilizing a power distribution in the latent space, and trained on galaxies from IllustrisTNG simulation.
  arXiv  Detail & Related papers  (2024-06-06T07:34:58Z)
• 3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes [68.66237114509264]
  We present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space.
  arXiv  Detail & Related papers  (2023-04-22T19:28:49Z)
• NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images [17.637064969966847]
  NeAT is a new neural rendering framework that learns implicit surfaces with arbitrary topologies from multi-view images. NeAT supports easy field-to-mesh conversion using the classic Marching Cubes algorithm. Our approach is able to faithfully reconstruct both watertight and non-watertight surfaces.
  arXiv  Detail & Related papers  (2023-03-21T16:49:41Z)
• NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation [37.977032771941715]
  We propose a novel Head-Eye redirection parametric model based on Neural Radiance Field. Our model can decouple the face and eyes for separate neural rendering. It can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction.
  arXiv  Detail & Related papers  (2022-12-30T13:52:28Z)
• ImpDet: Exploring Implicit Fields for 3D Object Detection [74.63774221984725]
  We introduce a new perspective that views bounding box regression as an implicit function. This leads to our proposed framework, termed Implicit Detection or ImpDet. Our ImpDet assigns specific values to points in different local 3D spaces, thereby high-quality boundaries can be generated.
  arXiv  Detail & Related papers  (2022-03-31T17:52:12Z)
• Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video [66.94502090429806]
  An object's interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations.
  arXiv  Detail & Related papers  (2021-04-06T18:05:27Z)
• Neural Topological SLAM for Visual Navigation [112.73876869904]
  We design topological representations for space that leverage semantics and afford approximate geometric reasoning. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.
  arXiv  Detail & Related papers  (2020-05-25T17:56:29Z)
• Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation [76.21696417873311]
  We introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space. CCNs extract a view-specific feature through a view-specific convolutional kernel to predict object category scores at each viewpoint. Our experiments demonstrate the effectiveness of the cylindrical convolutional networks on joint object detection and viewpoint estimation.
  arXiv  Detail & Related papers  (2020-03-25T10:24:58Z)
• 3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface [1.9336815376402714]
  A novel, adaptive ground-aware, and cost-effective 3D Object Detection pipeline is proposed. A new state-of-the-art 3D object detection performance among the two-stage Lidar Object Detection pipelines is proposed.
  arXiv  Detail & Related papers  (2020-02-02T05:42:23Z)

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.