5 accepted papers
Human comprehension of a video stream is naturally broad: in a few instants we are able to understand what is happening the relevance and relationship of objects and forecast what will follow in the near future everything all at once. We believe that - to effectively transfer such an holistic percep…
We introduce MeshGPT a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields. Inspired by recent advances in powerful large language models we adopt a seque…
In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilitie…
The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting from simulated datasets and have shown promising performance in real-world scenarios. Nevertheless, many of them still
In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region around it. Local and global information are encoded in a combined embedding. A denoising pretext task provides the networ…