Locating 3D Object Proposals: A Depth-Based Online Approach
Ramanpreet Singh Pahwa, Jiangbo Lu, Nianjuan Jiang, Tian Tsong Ng,, Minh N. Do

TL;DR
This paper introduces a real-time online method for generating accurate 3D object proposals from RGB-D video sequences, leveraging depth and multi-view information to improve over existing techniques.
Contribution
A novel online approach that uses depth data and multi-view registration to produce precise 3D object proposals in near real-time.
Findings
Significantly more accurate than current state-of-the-art methods.
Operates in less than a second on standard hardware.
Suitable for integration into SLAM systems for quick 3D localization.
Abstract
2D object proposals, quickly detected regions in an image that likely contain an object of interest, are an effective approach for improving the computational efficiency and accuracy of object detection in color images. In this work, we propose a novel online method that generates 3D object proposals in a RGB-D video sequence. Our main observation is that depth images provide important information about the geometry of the scene. Diverging from the traditional goal of 2D object proposals to provide a high recall (lots of 2D bounding boxes near potential objects), we aim for precise 3D proposals. We leverage on depth information per frame and multi-view scene information to obtain accurate 3D object proposals. Using efficient but robust registration enables us to combine multiple frames of a scene in near real time and generate 3D bounding boxes for potential 3D regions of interest.…
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