Since many safety-critical systems, such as surgical robots and autonomous driving cars operate in unstable environments with sensor noise incomplete data, it is desirable for object detectors to take the localization uncertainty into account. However, there are several limitations of existing estimation methods anchor-based detection. 1) They model heterogeneous properties different characteri...

Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive from them. However, the are inclined get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such introducing redundant noise, mismatching detracting robustness of de...

Recently, promising applications in robotics and augmented reality have attracted considerable attention to 3D object detection from point clouds. In this paper, we present FCAF3D - a first-in-class fully convolutional anchor-free indoor method. It is simple yet effective method that uses voxel representation of cloud processes voxels with sparse convolutions. can handle large-scale scenes mini...

Estimating the uncertainty of a neural network plays fundamental role in safety-critical settings. In perception for autonomous driving, measuring means providing additional calibrated information to downstream tasks, such as path planning, that can use it towards safe navigation. this work, we propose novel sampling-free estimation method object detection. We call CertainNet, and is first prov...