I wrote it in quotes for a reason, I certainly did not mean see as we perceive, but "see" as how FSD does it.
From my impression of the system, FSD identifies objects. LiDAR maps distances to objects, but I wonder if it can identify objects.
FSD uses digital cameras which are a 2D grid of pixels. FSD identifies objects by data processing. LiDAR is a 3D cloud of returned data, objects are identified by data processing. Quite different processing but the camera does not have any magic difference that makes identifying objects somehow more obvious.
Here is an article I google-picked at random.
Deep Multi-modal Object Detection for Autonomous Driving
Amal Ennajar, Nadia Khouja, Rémi Boutteau, Fethi Tlili
Cameras and LiDARs have complementary characteristics that make camera-LiDAR combination models
more viable and Well-known compared to other sensor combination setups (radar-camera, LiDAR-radar, etc.,). To be more specific, vision-based recognition frameworks accomplish palatable performance at low-cost, regularly beating human experts. Nevertheless,
a mono-camera discernment framework cannot give a solid 3D geometry, which is required for self-driving. On the other hand, stereo cameras can give 3D geometry, but do so at a high computational cost and fail in high-occlusion and texture-less situations. Most later sensor combination strategies focus on harnessing LiDAR and camera for 3D object detection. PointFusion [13] is a generic 3D object detection method that exploits both image and 3D point cloud information. It processes the image and LiDAR information using a CNN and a PointNet architectures and then generates 3D bounding boxes using the extracted features.
...
LiDARs give precise 3D estimations at near range,
but the coming about point cloud gets to be scanty
at long extend, decreasing the system capacity to pre-
cisely identify far off objects. Cameras offer wealthy
appearance characteristics, but are not a great source
of data for depth estimation. These extra highlights
have made LiDAR-camera sensor combination a theme
of investigation in recent years. This combination
has been demonstrated to attain high accuracy in
3D object detection for numerous applications, count-
ing autonomous driving, but it has its impediments.
Both cameras and LiDARs are touchy to unfavorable
climate conditions (eg snow, fog, rain), which can
radically decrease their perception range and detection
capabilities. Moreover, LiDARs and cameras are not
able to identify the speed of objects without utilizing
temporal data. Estimating the speed of objects may be
a necessity to maintain a security distance to avoid
collisions in numerous scenarios, and depending on
temporal information may not be a doable arrangement
in time. For a long time, radars have been utilized
in vehicles for ADAS (Advanced Driver Assistance
System) applications to avoid collision and control
velocity. Compared to LiDARs and cameras, radars are
exceptionally strong to unfavorable climate conditions
and are able to distinguish objects at exceptionally long
extend (up to 200 meters for car radars). Radars utilize
the Doppler effect to precisely gauge the speeds of all
identified objects, without requiring any temporal data.
In addition, compared to LiDARs, radar point clouds
require less handling time and recently they can be
utilized as object detection results. These highlights
and their lower cost compared to LiDARs have made
radars a prevalent sensor in autonomous driving appli-
cations. Few research has focused on combining radar
information with other sensors.
