How do you deal with occlusion and occlusion management in AR?

Using occlusion effectively can blend the physical world with the virtual world. When virtual objects are effectively occluded the result can be a more believable and immersive experience. Apple's ARKit can detect humans and furniture allowing content to appear behind them. Current advancement's in AI assisted "Segment Anything" will allow almost anything in the camera's foreground to be separated from the background. This will allow AR experiences that merge content seamlessly into an environment.

Occlusion is key to making an Augmented Reality experience blend seamlessly into the real world. If a 3D animation is not occluded properly the immersion is immediately broken. Anything that includes an object placed on the user’s head such as a hat must include an occluder to ensure the experience looks realistic.

Depth Sensing: Modern AR devices come with depth sensors that can create a depth map of the environment. This map can be used to determine which objects are in front of others. Depth maps can be used to mask out areas of virtual content that should be hidden behind real-world objects. Segmentation: Use image segmentation techniques to divide the camera feed into different regions, each corresponding to a different object or part of the scene. Once segmented, you can determine which segments should be in front of virtual objects. Real-time Shadow Casting: Shadows can provide cues about the relative positions of objects. By casting shadows of virtual objects onto real-world objects (or v.v), you can enhance the perception of occlusion.

Depth-based Masking: Use depth maps from sensors to mask AR content, hiding parts occluded by real objects. Stencil Buffers: Define screen regions to control where virtual objects render. Real-time Shadows: Render shadows of virtual objects onto real ones for depth cues. Physics Interactions: Simulate realistic interactions between virtual and real objects. Alpha Blending: Render partially occluded virtual objects semi-transparently. 3D Reconstruction: Model the real-world's 3D geometry to position virtual objects accurately. Temporal Consistency: Ensure consistent occlusion handling across frames. Machine Learning: Train models to predict and handle occlusion. Indicators: Use visual or auditory cues to signal occluded virtual objects

I find the most effective and stable occlusion technique is the use of Machine Learning (ML) segmentation. ML allows developers to decide exactly what to occlude in a scene. Additionally the use of Lidar can detect where an object would realistically be when integrated into the scene.

Make sure that you know what distance your experiences are from the user. For example, an experience designed for the sky may rely on sky segmentation while a selfie facing experience may rely on body segmentation.

Improve occlusion in your scene by utilizing different techniques at the same time. You can utilize Machine Learning, body tracking, hand tracking, etc. If you need to segment out a person accurately you can use a pair of segmentation and a body tracked 3D mesh with an occluder material on top. The combination may not be entirely necessary, but depending on the project it can improve stability.

Occluder materials are an easy to implement technique that exists in most AR platforms. It is a material that is used to hide anything behind it esentially using it as a mask in a scene. An occluder can be added to any 3D mesh. This is how most programs create an accurate 3D head track. The make a 3D model that replicates a human head, tracks it to the user, and places an occluder material on top to cut it from the scene.