Unsupervised learning of 3d objects in the wild - Wu, S (2022)
We live in a physical 3D world. Being able to perceive the world in 3D allows us to confidently navigate and perform sophisticated actions in it. Developing 3D perception systems is not only key to many AR and robotics applications, but also fundamental to the goal of visual understanding. However, most existing learning-based image understanding models process images simply as compositions of 2D patterns, ignoring the fact that images arise from a physical 3D world. Recent advances in differentiable rendering have opened new possibilities for learning physically-grounded disentangled 3D representations from raw images, marrying geometry with learning at last.