Search Results for author:
Found 7 papers, 3 papers with code
A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
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.
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
In contrast to methods that use alternate 3D shape representations (e. g. implicit representations), our approach is a discrete denoising diffusion probabilistic model that operates natively on the polygonal mesh data structure.
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
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.
OpenPatch: a 3D patchwork for Out-Of-Distribution detection
We advance the field by introducing OpenPatch that builds on a large pre-trained model and simply extracts from its intermediate features a set of patch representations that describe each known class.
3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems.
Denoise and Contrast for Category Agnostic Shape Completion
The combined embedding inherits category-agnostic properties from the chosen pretext tasks.
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials.
