4 accepted papers
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…
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…