论文标题
深度贝叶斯语义场景完成
In Depth Bayesian Semantic Scene Completion
论文作者
论文摘要
这项工作研究语义场景的完成，旨在预测我们周围环境的3D语义分割，即使某些区域被遮住了。为此，我们构建了一个贝叶斯卷积神经网络（BCNN），它不仅能够执行分割，而且还可以预测模型的不确定性。这是标准CNN中不存在的重要功能。 我们在MNIST数据集上显示，贝叶斯方法在处理准确性，精度和回忆时在训练阶段的处理阶段，在处理阶段不见时，贝叶斯方法的性能与标准CNN相等或更好。具有更好的校准得分和表达模型不确定性的能力的额外好处。 然后，我们将显示语义场景完成任务的结果，其中在SUNCG数据集中在测试时间引入类别。在这项更复杂的任务中，贝叶斯方法的表现优于标准CNN。在平均精度和分离得分方面表现出更好的交点比联合得分更好。
This work studies Semantic Scene Completion which aims to predict a 3D semantic segmentation of our surroundings, even though some areas are occluded. For this we construct a Bayesian Convolutional Neural Network (BCNN), which is not only able to perform the segmentation, but also predict model uncertainty. This is an important feature not present in standard CNNs. We show on the MNIST dataset that the Bayesian approach performs equal or better to the standard CNN when processing digits unseen in the training phase when looking at accuracy, precision and recall. With the added benefit of having better calibrated scores and the ability to express model uncertainty. We then show results for the Semantic Scene Completion task where a category is introduced at test time on the SUNCG dataset. In this more complex task the Bayesian approach outperforms the standard CNN. Showing better Intersection over Union score and excels in Average Precision and separation scores.
