> For the complete documentation index, see [llms.txt](https://docs.imerit.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.imerit.net/3d-multi-sensor-fusion/labeling/3d-multi-sensor-fusion-labeling-editor/key-features/point-projection-and-frustum.md).
# Point Projection and Frustum
## Point Projection
Raycaster is the point projection of the cursor coordinates in the point cloud over the images in the image panel to help annotators identify the object swiftly. It appears as a "red dot" over the camera images while hovering the cursor over the points in the point cloud.
## Field of View (FoV)
Field of View highlights point cloud sections visible in a selected camera image, helping annotators quickly identify which 3D points correspond to that specific camera's perspective for accurate annotation.
## 2D to 3D Projection / Ray Beam
When point cloud data is sparse or an object is occluded, annotators often struggle to confidently locate it directly in the 3D view. This feature reverses the existing point-to-image projection: once an annotator identifies and selects the object in a camera image, the tool projects that selection back into the point cloud, highlighting the corresponding region using the sensor's calibration data. This removes the need to manually cross-reference or realign the point cloud view based on visual guesswork.
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### **Steps to Use**
1. The user identifies the object of interest in one of the camera images in the Image Panel.
2. The user right-clicks the image on the thumbnail and enables Ray.
3. The user hovers over the image (either thumbnail or Image Viewer).
1. Using calibration data linking the camera to the point cloud, the tool projects the selected region into 3D space.
4. The corresponding area in the point cloud is highlighted with a ray, giving the user a clear starting point to draw or adjust their annotation.
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### Benefits
This closes the loop between 2D and 3D workflows: rather than only projecting point cloud data onto images for verification, annotators can now go the other direction, using the image as the more reliable reference and letting the tool locate the matching 3D region. This is especially useful for distant, small, or partially visible objects where LiDAR returns alone are not enough to confidently place an annotation.
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