Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility

Benjamin Hepp, Debadeepta Dey, Sudipta N. Sinha, Ashish Kapoor, Neel Joshi, Otmar Hilliges; The European Conference on Computer Vision (ECCV), 2018, pp. 437-452


Camera equipped drones are nowadays being used to explore large scenes and reconstruct detailed 3D maps. When free space in the scene is approximately known, an offline planner can generate optimal plans to efficiently explore the scene. However, for exploring unknown scenes, the planner must predict and maximize usefulness of where to go on the fly. Traditionally, this has been achieved using handcrafted utility functions. We propose to learn a better utility function that predicts the usefulness of future viewpoints. Our learned utility function is based on a 3D convolutional neural network. This network takes as input a novel volumetric scene representation that implicitly captures previously visited viewpoints and generalizes to new scenes. We evaluate our method on several large 3D models of urban scenes using simulated depth cameras. We show that our method outperforms existing utility measures in terms of reconstruction performance and is robust to sensor noise.

Related Material

author = {Hepp, Benjamin and Dey, Debadeepta and Sinha, Sudipta N. and Kapoor, Ashish and Joshi, Neel and Hilliges, Otmar},
title = {Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}