3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations

Understand

We propose a system that learns to detect objects and infer their 3D poses in RGB-D images.

• Many existing systems can identify objects and infer 3D poses, but they heavily rely on human labels and 3D annotations.
• The challenge here is to achieve this without relying on strong supervision signals.
• To address this challenge, we propose a model that maps RGB-D images to a set of 3D visual feature maps in a differentiable fully-convolutional manner, supervised by predicting views.