10 accepted papers
Accurate and reliable positioning is essential for perception, decision-making, and other high-level applications in autonomous driving, autonomous aerial vehicles, and intelligent robotics. Due to the inherent limitations of standalone sensors, integrating heterogeneous sensors with complementary c…
Out-of-Distribution (OOD) detection requires accurately classifying In-Distribution (ID) samples while effectively distinguishing anomalous OOD data. However, existing methodologies predominantly rely on real-valued magnitude features, neglecting the semantic richness embedded in phase information, …
Accurate and reliable reference poses are a crucial foundation for numerous scientific and engineering applications. This study introduces a two-stage reference pose generation method to produce a more reliable trajectory for large-scale outdoor environments. The first stage is a tightly coupled GNS
One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, as traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation le…
In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3.6 billion precisely point-level annotated points covering an expansive area of 30000m^2. It encompasses three prevalent daily environments including learning scenes working scenes and living scenes.…
The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its practicality. In this work we propose Random Prompt Assisted Weakly-supervised 3D Object Detection termed as Prompt3D a weakly-supervised approach that leverages position-level labels to overcome this chal…
Many medical images domains suffer from inherent ambiguities. A feasible approach to resolve the ambiguity of lung nodule in the segmentation task is to learn a distribution over segmentations based on a given 2D lung nodule image. Whereas lung nodule with 3D structure contains dense 3D spatial info…
In this paper, we propose a novel feature decoupling method to tackle two critical problems in the lung nodule segmentation task: (i) ambiguity of nodule boundary leads to the imprecise segmentation boundary and (ii) the high false positive rate of segmentation result. Our motivation is that an accu…
Pulmonary nodule detection and segmentation are the necessary successively steps in lung cancer screening with low-dose computed tomography (CT) scans. However, the state-of-the-art models focus on solving tasks separately, thereby ignore the correlation between each task. Besides, most nodule detec…
Semantic segmentation has attracted increasing attention due to its important role in self-driving, and it is often realized by supervised learning with large number of well labeled maps. However, the labeled images are hard to be obtained in most circumstances, and the common way for unsupervised s…