Automated three-dimensional (3D) object reconstruction is the task of
building a geometric representation of a physical object by means of sensing
its surface. Even though new single view reconstruction techniques can predict
the surface, they lead to incomplete models, specially, for non commons objects
such as antique objects or art sculptures. Therefore, to achieve the task's
goals, it is essential to automatically determine the locations where the
sensor will be placed so that the surface will be completely observed. This
problem is known as the next-best-view problem. In this paper, we propose a
data-driven approach to address the problem. The proposed approach trains a 3D
convolutional neural network (3D CNN) with previous reconstructions in order to
regress the \btxt{position of the} next-best-view. To the best of our
knowledge, this is one of the first works that directly infers the
next-best-view in a continuous space using a data-driven approach for the 3D
object reconstruction task. We have validated the proposed approach making use
of two groups of experiments. In the first group, several variants of the
proposed architecture are analyzed. Predicted next-best-views were observed to
be closely positioned to the ground truth. In the second group of experiments,
the proposed approach is requested to reconstruct several unseen objects,
namely, objects not considered by the 3D CNN during training nor validation.
Coverage percentages of up to 90 \% were observed. With respect to current
state-of-the-art methods, the proposed approach improves the performance of
previous next-best-view classification approaches and it is quite fast in
running time (3 frames per second), given that it does not compute the
expensive ray tracing required by previous information metrics.

本文提出了一种基于卷积神经网络的数据驱动方法来解决 3D 物体重建中的下一个最佳视角问题，验证实验表明，该方法在预测下一个最佳视角、提高物体重建覆盖率和提高运算速度等方面，都有很高的性能表现。

使用 3D 卷积神经网络进行下一个最佳视角回归

Next-best-view Regression using a 3D Convolutional Neural Network

A multi-view image sequence provides a much richer capacity for object
recognition than from a single image. However, most existing solutions to
multi-view recognition typically adopt hand-crafted, model-based geometric
methods, which do not readily embrace recent trends in deep learning. We
propose to bring Convolutional Neural Networks to generic multi-view
recognition, by decomposing an image sequence into a set of image pairs,
classifying each pair independently, and then learning an object classifier by
weighting the contribution of each pair. This allows for recognition over
arbitrary camera trajectories, without requiring explicit training over the
potentially infinite number of camera paths and lengths. Building these
pairwise relationships then naturally extends to the next-best-view problem in
an active recognition framework. To achieve this, we train a second
Convolutional Neural Network to map directly from an observed image to next
viewpoint. Finally, we incorporate this into a trajectory optimisation task,
whereby the best recognition confidence is sought for a given trajectory
length. We present state-of-the-art results in both guided and unguided
multi-view recognition on the ModelNet dataset, and show how our method can be
used with depth images, greyscale images, or both.

本文介绍了一种将卷积神经网络应用到多视角识别中的方法，可以有效地解决任意摄像机轨迹上的识别问题，并且在 ModelNet 数据集上取得了最先进的结果。同时，该方法也可以在活动识别中应用。