Physical Primitive Decomposition

Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition---understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu

Superquadrics for segmentation and modeling range data

We present a novel approach to reliable and efficient recovery of part-descriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is based on the recover-andselect paradigm. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise

CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.Comment: Accepted at CVPR-201

Determining geometric primitives for a 3D GIS : easy as 1D, 2D, 3D?

Acquisition techniques such as photo modelling, using SfM-MVS algorithms, are being applied increasingly in several fields of research and render highly realistic and accurate 3D models. Nowadays, these 3D models are mainly deployed for documentation purposes. As these data generally encompass spatial data, the development of a 3D GIS would allow researchers to use these 3D models to their full extent. Such a GIS would allow a more elaborate analysis of these 3D models and thus support the comprehension of the objects that the features in the model represent. One of the first issues that has to be tackled in order to make the resulting 3D models compatible for implementation in a 3D GIS is the choice of a certain geometric primitive to spatially represent the input data. The chosen geometric primitive will not only influence the visualisation of the data, but also the way in which the data can be stored, exchanged, manipulated, queried and understood. Geometric primitives can be one-, two- and three-dimensional. By adding an extra dimension, the complexity of the data increases, but the user is allowed to understand the original situation more intuitively. This research paper tries to give an initial analysis of 1D, 2D and 3D primitives in the framework of the integration of SfM-MVS based 3D models in a 3D GIS