Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction

Yuxuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu, P. Abbeel, Xi Chen
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引用次数: 3

Abstract

. 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 improve-ment 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 3 .
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三维边界盒预测的自回归不确定性建模
. 在许多计算机视觉应用中,三维边界框是一种广泛的中间表示。然而,预测它们是一项具有挑战性的任务,主要是由于部分可观测性,这激发了对强烈不确定性的需求。虽然许多最近的方法已经探索了更好的架构来消费稀疏和非结构化的点云数据,但我们假设在输出分布的建模方面有改进的空间,并探索如何使用自回归预测头来实现这一点。此外,我们发布了一个模拟数据集COB-3D,它突出了现实世界机器人应用中出现的新型模糊性,其中3D边界盒预测在很大程度上尚未得到充分探索。我们提出了利用我们的自回归模型进行高置信度预测和有意义的不确定性测量的方法,在SUN-RGBD、Scannet、KITTI和我们的新数据集3上取得了强有力的结果。
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