2 research outputs found
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette
3D shape reconstruction from a single image is a highly ill-posed problem.
Modern deep learning based systems try to solve this problem by learning an
end-to-end mapping from image to shape via a deep network. In this paper, we
aim to solve this problem via an online optimization framework inspired by
traditional methods. Our framework employs a deep autoencoder to learn a set of
latent codes of 3D object shapes, which are fitted by a probabilistic shape
prior using Gaussian Mixture Model (GMM). At inference, the shape and pose are
jointly optimized guided by both image cues and deep shape prior without
relying on an initialization from any trained deep nets. Surprisingly, our
method achieves comparable performance to state-of-the-art methods even without
training an end-to-end network, which shows a promising step in this direction
Neural Object Descriptors for Multi-View Shape Reconstruction
The choice of scene representation is crucial in both the shape inference
algorithms it requires and the smart applications it enables. We present
efficient and optimisable multi-class learned object descriptors together with
a novel probabilistic and differential rendering engine, for principled full
object shape inference from one or more RGB-D images. Our framework allows for
accurate and robust 3D object reconstruction which enables multiple
applications including robot grasping and placing, augmented reality, and the
first object-level SLAM system capable of optimising object poses and shapes
jointly with camera trajectory