I’ve been diving into this really challenging problem involving 3D grids and line of sight, and I could use some help from anyone who’s tackled something similar. So here’s the deal: I have a 3D surface represented by a grid of cells, which I’ve set up in a 3D matrix. The cells are marked as “surface cells” if they have nonzero entries, meaning that they actually represent part of the surface. The tricky part is that the grid isn’t just a simple, Cartesian 3D space where everything is nice and square; sometimes it’s spherical with wedges, and sometimes it’s even more complex than that.
What I really need to figure out is how to efficiently determine the line of sight between points in this 3D grid. The key requirement is that the points I’m interested in—the ones not on or inside the surface—must have direct visible lines to surface points without intersecting the surface itself. So a point on the surface is “visible” if I can draw a straight line to it from my point, and that line doesn’t cut through any of those surface cells.
The challenge gets tougher with larger grids and varied geometries. I want to do this efficiently because brute-forcing through every possibility could take forever, especially with a dense grid or a complex surface. I’ve thought about a few approaches, like raycasting or maybe even some kind of spatial partitioning to reduce the number of checks I have to make, but I haven’t settled on a solution that I feel confident about.
Does anyone have insights on methods you’ve used to tackle this? Have you found certain algorithms or techniques that really shine in this sort of application? Any thoughts on handling different grid shapes while still keeping it efficient would be incredibly helpful. Thanks!
An effective approach for efficiently determining line of sight in complex, non-uniform 3D grids involves hybridizing raycasting with spatial partitioning methods such as octrees, BSP trees, or bounding volume hierarchies (BVH). Octrees or BVHs can significantly reduce the computational overhead by quickly discarding large empty regions and allowing for targeted intersection tests against surface cells. Using raycasting complemented by spatial data structures will enable you to perform rapid intersection tests by traversing space hierarchically rather than iterating over every individual cell. Additionally, leveraging precomputed visibility data or hierarchical spatial query structures adapted specifically for non-uniform or spherical grids could further enhance performance by reducing redundant calculations.
Given your complex geometries—especially spherical or wedge-based grids—integrating coordinate mappings or parametric space transformations can simplify intersection tests while maintaining accuracy. It may also be beneficial to employ efficient ray traversal schemes, like the Amanatides-Woo algorithm adapted to non-uniform grids, to handle irregular cell sizes and shapes efficiently. Combining spatial partitioning, optimized ray traversal, and geometry-specific transformations can result in substantial performance gains and scalability, helping you ensure accurate visibility determination even in densely populated, varied grid environments.
Hey there! So, I’ve been wrestling with a similar problem regarding 3D grids and figuring out line of sight, and I think I can share some thoughts!
One method I was considering is using raycasting. Basically, you can shoot a ray from your point to each of the surface points you want to check visibility for. If the ray intersects any surface cells, then that point isn’t visible. You’d just need to ensure that the raycasting algorithm you choose works well with the non-Cartesian shapes—like spherical or irregular geometries you mentioned.
I’ve also read about using spatial partitioning techniques such as Octrees. It can really help in breaking down your space into manageable chunks. This way, you can quickly eliminate large sections of the grid that don’t contain surface cells, which should save you a ton of computation time!
Another thing is to check if you can use some kind of visibility algorithm like the BSP (Binary Space Partitioning) tree. I think it could be useful because it organizes objects in your grid and can help determine visibility in complex environments. It might be a bit complicated at first, but it could be worth it!
Maybe try combining these methods too? Like, use a spatial partitioning approach to limit the number of rays you cast from your point, focusing only on relevant regions of your grid. Just a thought!
Trying not to get too overwhelmed by the complexity is key. Start with simpler shapes if you can and build your way up to more complicated ones as you tweak your algorithms. I hope some of this helps you get unstuck!