论文标题
3D边界框预测的自回旋不确定性建模
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
论文作者
论文摘要
在许多计算机视觉应用程序中，3D边界框是广泛的中间表示。但是，预测它们是一项具有挑战性的任务，这主要是由于部分可观察性，这激发了强烈的不确定性意识。尽管许多最近的方法探索了更好地消费稀疏和非结构化点云数据的架构，但我们假设在输出分布建模并探索如何使用自动回应预测头可以实现这一目标。此外，我们发布了一个模拟数据集COB-3D，该数据集强调了在现实世界机器人应用中出现的新类型的歧义类型，其中3D边界框的预测在很大程度上没有被驱散。我们提出了利用自回旋模型来进行高置信度预测和有意义的不确定性度量的方法，从而对Sun-RGBD，Scannet，Kitti和我们的新数据集取得了强劲的结果。
3D bounding boxes are a widespread intermediate representation in many computer vision applications. However, predicting them is a challenging task, largely due to partial observability, which motivates the need for a strong sense of uncertainty. While many recent methods have explored better architectures for consuming sparse and unstructured point cloud data, we hypothesize that there is room for improvement in the modeling of the output distribution and explore how this can be achieved using an autoregressive prediction head. Additionally, we release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications, where 3D bounding box prediction has largely been underexplored. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures, achieving strong results on SUN-RGBD, Scannet, KITTI, and our new dataset.
