Here we describe the method used to find the extrinsics and intrinsics defining the captured dataset. An overview of the method can be found in the Algorithm 1.

The ability to sense and reconstruct 3D appearance and geometry is critical to applications in vision, graphics, and beyond.

Neural networks trained on biased datasets tend to inadvertently learn spurious correlations, hindering generalization.

We study lower bounds on adaptive sensing algorithms for recovering low rank matrices using linear measurements.

The inherent assumption in IO is that an agent generates its decision by solving an optimization problem.

We conduct a novel theoretical analysis to demonstrate CODA's

Work done during an internship at Tencent AI Lab.

Recently, 3D point cloud deep learning has been making remarkable strides in various domains, e.g.,