Active Implicit Reconstruction Using One-Shot View Planning
Hao Hu, Sicong Pan, Liren Jin, Marija Popovi\'c, Maren Bennewitz

TL;DR
This paper introduces a novel one-shot view planning method using implicit representations and deep learning to efficiently reconstruct objects with limited views, outperforming existing methods.
Contribution
It proposes integrating implicit representations into one-shot view planning with a neural network that predicts optimal views directly, reducing the need for iterative planning.
Findings
Achieves high-quality object reconstruction with limited views.
Outperforms baseline methods in simulated experiments.
Demonstrates effectiveness in real-world scenarios.
Abstract
Active object reconstruction using autonomous robots is gaining great interest. A primary goal in this task is to maximize the information of the object to be reconstructed, given limited on-board resources. Previous view planning methods exhibit inefficiency since they rely on an iterative paradigm based on explicit representations, consisting of (1) planning a path to the next-best view only; and (2) requiring a considerable number of less-gain views in terms of surface coverage. To address these limitations, we propose to integrate implicit representations into the One-Shot View Planning (OSVP). The key idea behind our approach is to use implicit representations to obtain the small missing surface areas instead of observing them with extra views. Therefore, we design a deep neural network, named OSVP, to directly predict a set of views given a dense point cloud refined from an…
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Taxonomy
TopicsRobotic Mechanisms and Dynamics · Soft Robotics and Applications · Image and Object Detection Techniques
