Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Scene statistics and 3D surface perception

The inference of depth information from single images is typically performed by devising models of image formation based on the physics of light interaction and then inverting these models to solve for depth. Once inverted, these models are highly underconstrained, requiring many assumptions such as Lambertian surface reflectance, smoothness of surfaces, uniform albedo, or lack of cast shadows....

3D Scene Comparison using Topological Graphs

New technologies for shape acquisition and rendering of digital shapes have simplified the process of creating virtual scenes; nonetheless, shape annotation, recognition and manipulation of both the complete virtual scenes and even of subparts of them are still open problems. In this paper we deal with the problem of comparing two (or more) object sets, where each model is represented by an att...

3D Dynamic Scene Reconstruction from Multi-View Image Sequences

Learning Spatial Knowledge for Text to 3D Scene Generation

We address the grounding of natural language to concrete spatial constraints, and inference of implicit pragmatics in 3D environments. We apply our approach to the task of text-to-3D scene generation. We present a representation for common sense spatial knowledge and an approach to extract it from 3D scene data. In text-to3D scene generation, a user provides as input natural language text from ...

6D SLAM with Kurt 3D

technology, equipment and applications related to the automated assembly and robotics industries. Feature articles include updates on the latest manufacturing research, tutorials on a particular manufacturing technology and field reports on installed manufacturing technologies, all from a technical perspective. Each issue also includes additional brief articles, product announcements and event ...