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About Gumgo

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  1. I am creating procedurally generated levels by stitching together "tilesets" created in a 3D modeling program such as 3DS max. Each tile consists of a 2x2x2 "section" of geometry, such as a wall, floor, corner, etc. These tiles are placed next to each other to form rooms and hallways.   After all the tiles are placed, I run a mesh simplification algorithm over the resulting geometry to reduce polygon counts for rendering and physics (and eventually NavMesh generation). The algorithm goes something like this: 1) Form groups of adjacent coplanar triangles that all have the same UV barycentric parameterizations (e.g. removing vertices wouldn't cause "warping"). 2) Combine each group into a single polygon, possibly with holes. 3) Remove excess collinear vertices from the boundaries. 4) Triangulate the polygons using constrained delaunay triangulation.   The issue is that step 4) is prone to producing long skinny triangles, which is causing problems everywhere (e.g. breaking my thresholds used to detect collinearity). Can anyone provide some advice on how to approach this problem, or point me to some resources or algorithms that deal with avoiding this?
  2. Ah thanks, that sounds like a way better idea! It makes more sense for the server to simply tell each client how far off they are, rather than the clients trying to guess. I'll go ahead and implement that!
  3. I'm performing client side prediction, so the clients need to run ahead of the server. The clients need to run ahead far enough so that their input packets are received by the server slightly before they actually need to be executed. If they arrive too late, the server would either need to back up the world (which I'm not doing) or make some estimations (e.g. the player was moving forward so they probably still are) and try to fix things up as much as possible once the input packet does arrive (e.g. the player might jump 1 frame late).   I'm having some trouble determining the amount to predict by in a robust way that deals well with jitter and changing network conditions. Sometimes I "get it right" and the game runs well, but other times the prediction is a bit too short and there are a lot of input packets arriving too late, resulting client/server discrepancies (where the server is actually "wrong" in this case) and lots of client correction.   Call the prediction time P. A summary of the ideal behavior is: - P should be the smallest value which is large enough so that under "stable" conditions (even stable bad conditions), all input packets arrive to the server in time. - If there is a brief network glitch, it should be ignored and P should not be readjusted. - If network conditions change (e.g. RTT or jitter goes up) P should be readjusted.   Currently, my algorithm to determine prediction time P is as follows:   - Packets are sent at a fixed rate R. Let S = 1/R be the spacing between two packets (e.g. R = 30 packets/sec means S = 33ms). - The ping time and jitter of each outgoing packet is measured. The ping time is measured by taking the difference between the time the packet was sent and the time the ACK for the packet was received. Note that since packets (which contain ACKs) are sent with a spacing S, this means that the ping times will be somewhat "quantized" by S. To get an average ping time (RTT), I use an exponential moving average with a ratio of 0.1. To compute average jitter J, I take the differences between each packet's ping time and the current average RTT and compute the exponential moving variance (section 9 of this article). - I have RTT and J - even though they are averages they still fluctuate a bit, especially J. - In each packet, there is a server time update message. Call this T_p, for packet time. I could just let P = RTT + T_p but then P would be jittery because both RTT is changing and T_p has jitter to it. Instead, to compute P I do the following: - I compute P_f - the prediction value associated with this single frame, which will be jittery. My algorithm is P_f = T_p + RTT + 2*J + S. My reasoning for each term: RTT because we want to predict ahead of the server's time by RTT/2, 2*J because we want to add some padding for jitter, and S because we want to add some more padding for the quantization error described above. - Each time I compute a new P_f for the frame, I update P_a, which is an exponential moving average of P_f with ratio = 0.1. - On the very first frame, I set P to P_f, since I only have one data point. On subsequent frames, I increment P by 1, so that it is constantly and smoothly increasing, and I check whether P needs to be "re-synced": - I take the value D = abs( P - P_a ), the difference between the prediction time P and the average per-frame prediction times computed P_a, and test whether D > 2*J + S. If that expression is true, I "re-sync" P by assigning P = P_a. I picked the value 2*J + S with the reasoning that if our predicted time differs from the average per-frame predicted time by more than twice the jitter (with padding for quantization), then it's likely wrong.   Can anyone suggest improvements to this algorithm, or suggest a better algorithm? My main problem right now seems to be determining when to re-sync P. Performing too many re-sync results in glitches and correction, but if P is too low and I don't re-sync (because it is "close enough" to P_a) then that also results in glitches and correction.
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