Bilateral filtering considers samples depending on two weights (often the secondary weight is more or less a boolean function).

I.e. when using a gaussin blur on a SSAO map, you can make it depending on the depth buffer, that is, only pixels which are on a similar depth level then the target pixel are considered for blurring. Therefor you need more or less two filter criteria (both are more or less functions). Here's some pseudo code with a simple threshold function for depth:

[source lang="cpp"]// all weights should sum up to 1.0float weight_sum = 0;// result colorvec3 color = vec3(0);// sample depth and color for 0...7, where 4 is the centerif(abs(depth_0-depth_4) < threshold){  color += color_0 * weight_0;  weight_sum += weight_0;}if(abs(depth_1-depth_4) < threshold){  color += color_1 * weight_1;  weight_sum += weight_1;}...// consider weight sumcolor *= 1/weight_sum;[/source]

You can optimize this (ie. comparing 4 samples at once).

Bilateral filtering only considers a certain samples and not all (a secondary weight which depends on some other input). I.e. when using a gaussin blur on a SSAO map, you can make it depending on the depth buffer, that is, only pixels which are on a similar depth level then the target pixel are considered for blurring. Therefor you need more or less two filter criteria (both are more or less functions). Here's some pseudo code with a simple threshold function for depth:

[source lang="cpp"]// all weights should sum up to 1.0float weight_sum = 0;// result colorvec3 color = vec3(0);// sample depth and color for 0...7, where 4 is the centerif(abs(depth_0-depth_4) < threshold){  color += color_0 * weight_0;  weight_sum += weight_0;}if(abs(depth_1-depth_4) < threshold){  color += color_1 * weight_1;  weight_sum += weight_1;}...// consider weight sumcolor *= 1/weight_sum;[/source]

You can optimize this (ie. comparing 4 samples at once).

Bilateral filtering only considers a certain samples and not all (a secondary weight which depends on some other input). I.e. when using a gaussin blur on a SSAO map, you can make it depending on the depth buffer, that is, only pixels which are on a similar depth level then the target pixel are considered for blurring. Therefor you need more or less two filter criteria (both are more or less functions). Here's some pseudo code with a simple threshold function for depth:

[source lang="cpp"]// all weights should sum up to 1.0float weight_sum = 0;// result colorvec3 color = vec3(0);// sample depth and color for 0...7, where 4 is the current texelif(abs(depth_0-depth_4) < threshold){  color += color_0 * weight_0;  weight_sum += weight_0;}if(abs(depth_1-depth_4) < threshold){  color += color_1 * weight_1;  weight_sum += weight_1;}...// consider weight sumcolor *= 1/weight_sum;[/source]

You can optimize this (ie. comparing 4 samples at once).

Bilateral filtering only considers a certain samples and not all. I.e. when using a gaussin blur on a SSAO map, you can make it depending on the depth buffer, that is, only pixels which are on a similar depth level then the target pixel are considered for blurring. Therefor you need more or less two filter criteria (both are more or less functions). Here's some pseudo code with a simple threshold function for depth:

[source lang="cpp"]// all weights should sum up to 1.0float weight_sum = 0;// result colorvec3 color = vec3(0);// sample depth and color for 0...7, where 4 is the current texelif(abs(depth_0-depth_4) < threshold){  color += color_0 * weight_0;  weight_sum += weight_0;}if(abs(depth_1-depth_4) < threshold){  color += color_1 * weight_1;  weight_sum += weight_1;}...// consider weight sumcolor *= 1/weight_sum;[/source]

You can optimize this (ie. comparing 4 samples at once).

Bilateral filtering only considers a certain samples and not all. I.e. when using a gaussin blur on a SSAO map, you can make it depending on the depth buffer, that is, only pixels which are on a similar depth level then the target pixel are considered for blurring. Therefor you need more or less two filter criteria (both are more or less functions). Here's some pseudo code with a simple threshold function for depth:

[source lang="cpp"]// all weights should sum up to 1.0float weight_sum = 0;// result colorvec3 color = vec3(0);// sample depth and color for 0...7, where 4 is the current texelif(abs(depth_0-depth_4) < threshold){  color += color_0 * weight_0;}if(abs(depth_1-depth_4) < threshold){  color += color_1 * weight_1;}...// consider weight sumcolor *= 1/weight_sum;[/source]

Bilateral filtering only considers a certain samples and not all. I.e. when using a gaussin blur on a SSAO map, you can make it depending on the depth buffer, that is, only pixels which are on a similar depth level then the target pixel are considered for blurring. Therefor you need more or less two filter criteria (both are more or less functions). Here's some pseudo code with a simple threshold function for depth:

[source lang="cpp"]// all weights should sum up to 1.0float weight_sum = 0;// result colorvec3 color = vec3(0);// sample depth and color for 0...7, where 4 is the current texelif(abs(depth_0-depth_4)%lethreshold){  color += color_0 * weight_0;}if(abs(depth_1-depth_4)%lethreshold){  color += color_1 * weight_1;}...// consider weight sumcolor *= 1/weight_sum;[/source]