3 accepted papers
Adversarial attacks exploit the vulnerability of deep models against adversarial samples. Existing point cloud attackers are tailored to specific models, iteratively optimizing perturbations based on gradients in either a white-box or black-box setting. Despite their promising attack performance, th…
Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise distribution transformation, which generally requires hundreds or thousands of steps of the inverse diffusion trajectory to get a high-quality image. Recent progress in designing fast samplers for DPMs achieves a trade-…
Existing learning-based methods usually train a point cloud upsampling model with synthesized, paired sparse-dense point clouds. However, the distribution gap between synthesized and real data limits the performance and generalization. To solve this problem, we innovatively regard the upsamplig task…