I'm sympathetic to the idea that robotics should try to do more with cameras, as opposed to LiDAR. As you say, humans can do without LiDAR (and they don't even

I'm sympathetic to the idea that robotics should try to do more with cameras, as opposed to LiDAR. As you say, humans can do without LiDAR (and they don't even need stereo!). But in practice, there's a good case for LiDAR in real-world applications, at least for the near future: * The accuracy of depth from a good LiDAR sensor is hard to beat in accuracy, specially at longer ranges. Looking at a stereo point cloud and a LiDAR point clouds side by side, you can really see the difference. * LiDAR works in bad lighting, even complete darkness. (In fact it might work better in such conditions). For self-driving applications, you probably want to be able to see that deer crossing the road at night. * LiDAR will work on textureless surfaces, that traditionally are an issue for stereo/SfM. That said, there are plenty of issues with LiDAR: relatively sparse, doesn't work on certain surfaces like water or shiny metal, heavyweight (bad for drones). So at least for now, it makes sense to integrate with other sensing modalities. Disclaimer: I have published research on Deep Learning with LiDAR.