  Rapid advances in 2D perception have led to systems that accurately detect
objects in real-world images. However, these systems make predictions in 2D,
ignoring the 3D structure of the world. Concurrently, advances in 3D shape
prediction have mostly focused on synthetic benchmarks and isolated objects. We
unify advances in these two areas. We propose a system that detects objects in
real-world images and produces a triangle mesh giving the full 3D shape of each
detected object. Our system, called Mesh R-CNN, augments Mask R-CNN with a mesh
prediction branch that outputs meshes with varying topological structure by
first predicting coarse voxel representations which are converted to meshes and
refined with a graph convolution network operating over the mesh's vertices and
edges. We validate our mesh prediction branch on ShapeNet, where we outperform
prior work on single-image shape prediction. We then deploy our full Mesh R-CNN
system on Pix3D, where we jointly detect objects and predict their 3D shapes.
