Generative Query Networks

Generative Query Networks artwork



Video: Neural Scene Representation and Rendering

Science Article: Neural Scene Representation and Rendering

DeepMind Blog Post: Neural Scene Representation and Rendering

Datasets: Datasets For Neural Scene Representation and Rendering

keywords: 3D scene understanding, vision, scene representation, variational inference, generative models, ConvDRAW, approximate inference, scene uncertainty.


Scene representation—the process of converting visual sensory data into concise descriptions—is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.

Unsupervised Learning of 3D Structure from Images

Proposed framework: Left: Given an observed volume or image x and contextual
information c, we wish to infer a corresponding 3D representation h (which can be a volume or a
mesh). This is achieved by modeling the latent manifold of object shapes via the low-dimensional
codes z. In experiments we will consider unconditional models (i.e., no context), as well as models
where the context c is class or one or more 2D views of the scene. Right: We train a contextconditional
inference network (red) and object model (green). When ground-truth volumes are
available, they can be trained directly. When only ground-truth images are available, a renderer is
required to measure the distance between an inferred 3D representation and the ground-truth image.

NIPS2016 Article: Unsupervised Learning of 3D Structure from Images

keywords: 3D scene understanding, vision, scene representation, variational inference, generative models, ConvDRAW3D, Spatial Transformer, volumetric data, mesh, OpenGL, approximate inference, scene uncertainty.


A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.