5 accepted papers
Generating realistic 3D outdoor scenes is essential for applications in autonomous driving, virtual reality, environmental science, and urban development. Traditional 3D generation approaches using single-layer diffusion methods can produce detailed scenes for individual objects but struggle with hi…
"Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep generative models to address this issue, they often consume substantial computational resources with slow generation speeds…
3D object detection from point clouds is crucial in safety-critical autonomous driving. Although many works have made great efforts and achieved significant progress on this task, most of them suffer from expensive annotation cost and poor transferability to unknown data due to the domain gap. Recen…
"With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, \etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/del…
Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over mini-batches while onl…