8 accepted papers
The advancements in light detection and ranging (LiDAR) sensors and 3D object detection techniques have boosted their deployment in a wide range of applications, autonomous driving, in particular. However, it has been demonstrated that 3D object detection models based on deep neural networks exhibit…
Saliency maps play a major role in understanding the decision-making process of 3D models by illustrating the importance of individual points from the input to model predictions. However, saliency maps typically suffer from inaccuracies due to not considering the potential classification of contribu…
3D object tracking has become a popular research topic because of its broad application prospects. However, it remains a challenging task to advance the trustworthiness of deep trackers, caused by the complex network structure of black-box models. In this paper, a local explanation method for 3D obj…
Deep neural networks for 3D point clouds are receiving increasing attention. Recent works have shown that deep neural networks for 3D point clouds are vulnerable to adversarial attacks. However, existing adversarial attacks typically iteratively optimize a single sample to generate the adversarial p…
Considering the vulnerability of existing deep models in the adversarial scenario, the robustness of 3D target tracking is not guaranteed. In this paper, we present an efficient generation based adversarial attack, termed Adversary Distillation Network (AD-Net), which is able to distract a victim tr…
It is well-recognized that 3D visual tasks based on deep neural networks are vulnerable to adversarial attacks. Existing methods to generate adversarial examples are mainly developed from injecting imperceptible perturbations into the inputs. However, aggressive characteristic of geometric transform…
3D object tracking is the key of automatic driving. We propose a new 3D object tracking method called Transformer-Hausdorff Net (TH-Net). It contains three main modules: Feature Extraction, Feature Fusion, and Proposal Generation. The Feature Extraction module extracts features from the template and…
Head pose estimation from depth image is a challenging problem, considering its large pose variations, severer occlusions, and low quality of depth data. In contrast to existing approaches that take 2D depth image as input, we propose a novel deep regression architecture called Head PointNet, which…