130 citations · 297 across the 16 of their papers we have counts for
4 papers · 1 filter
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable ob…
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt, Oliver Groth, Aron Monszpart +4
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained…
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Benjamin Graham, Martin Engelcke, Laurens van der Maaten
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…