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IGNOR: Image-guided Neural Object Rendering
We propose a new learning-based novel view synthesis approach for scanne...
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Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
Understanding the 3D world is a fundamental problem in computer vision. ...
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Feature-Based Transfer Learning for Robotic Push Manipulation
This paper presents a data-efficient approach to learning transferable f...
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Contact Localization for Robot Arms in Motion without Torque Sensing
Detecting and localizing contacts is essential for robot manipulators to...
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Viewpoint Adaptation for Rigid Object Detection
An object detector performs suboptimally when applied to image data take...
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Hercules: An Autonomous Logistic Vehicle for Contact-less Goods Transportation During the COVID-19 Outbreak
Since December 2019, the coronavirus disease 2019 (COVID-19) has spread ...
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Animated Stickies: Fast Video Projection Mapping onto a Markerless Plane through a Direct Closed-Loop Alignment
This paper presents a fast projection mapping method for moving image co...
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Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects
The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads. To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem. Computer vision 3D reconstruction approaches project objects and the human body from the 2D image domain to 3D and perform 3D space intersection directly. However, this solution would not meet the accuracy requirement in applications due to projection error. Another standard approach is to train a neural network to infer touch actions from the collected visual data. This strategy would require significant amounts of training data to generalize over scale and viewpoint variations. A different approach to this problem is to identify whether a person has touched a defined object. In this work, we show that the solution to this problem can be straightforward. Specifically, we show that the contact between an object and a static surface can be identified by projecting the object onto the static surface through two different viewpoints and analyzing their 2D intersection. The object contacts the surface when the projected points are close to each other; we call this cross view projection consistency. Instead of doing 3D scene reconstruction or transfer learning from deep networks, a mapping from the surface in the two camera views to the surface space is the only requirement. For planar space, this mapping is the Homography transformation. This simple method can be easily adapted to real-life applications. In this paper, we apply our method to do office occupancy detection for studying the COVID-19 transmission pattern from an office desk in a meeting room using the contact information.
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