<i>you don&#x27;t have to do object recognition on the point cloud to know what you shouldn&#x27;t be bumping into - since the data comes back &quot;already 3D&quot;</i><p>This is true of any pointcloud system though. No system returns density though, so you still have to make assumptions about what is worth running into at what resolution. So if a leaf falls in front of your car, you don&#x27;t immediately slam on the brakes.<p>I don&#x27;t know of any vision systems that do object recognition immediately, in fact I&#x27;m not even sure that is possible - or desirable. I guess if you baked segmentation into the firmware it would serve as a fast classifier, but AFAIK nobody is even talking about doing that as you would need a very well trained vision library and classifier to deploy to FPGA.<p>I make the distinction because we build fairly dense pointclouds with monocular RGB cameras and VSLAM. LIDAR isn&#x27;t required anymore. LIDAR is faster with lower error tolerances today, but nowhere near as cheap or portable. I suspect monocular SLAM will overtake LIDAR in the next couple of years as it&#x27;s getting faster and more accurate everyday.