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SVGF
Demodulation
计算光照时忽略 BRDF 中的材质信息，避免引入高频变化。以 diffuse 为例：
\[\text{diffuse} = L \cdot \rho \cdot n \cdot l / \pi\] \[\text{demodulate albedo} = L \cdot n \cdot l / \pi\]Temporal Accumulation
和 TAA 的重投影类似，通过深度、法线和 Mesh ID 来 reject。
Variance Esitimation
在时间上积累像素亮度的一阶矩和二阶矩 $\mu_1$、$\mu_2$。
计算方差：
\[\operatorname{Var} = \mu_2 - \mu_1^2\]Edge-Avoiding Atrous Wavelets
Wavelet filter 用于大范围滤波。法线和深度作为 edge avoiding 算法的输入，避免模糊掉几何边缘。
\[\hat{c}_{i+1}(p) = \frac{ \sum_{q \in \Omega} h(q)\, w(p,q)\, \hat{c}_i(q) }{ \sum_{q \in \Omega} h(q)\, w(p,q) }\]vec3 sum = vec3(0);
float weight_sum = 0;
for q in neighborhood:
float weight = wavelet_kernel(q) // atrous wavelelt
* depth_weight(p, q) // edge stopping
* normal_weight(p, q)
* luminance_weight(p, q);
sum += weight * color[q];
weight_sum += weight;
color_out[p] = sum / weight_sum;
墙壁的几何边缘不会被模糊掉。
Luminance Edge Stopping Function
通过上面计算的 variance，避免模糊掉阴影细节：
\[w(p,q) = \exp\left( - \frac{ \left| l_i(p) - l_i(q) \right| }{ \sqrt{ g_{3 \times 3}\left( \operatorname{Var}\left(l_i(p)\right) \right) } } \right)\]- Variance 大 → 噪声多 → 增大模糊，降低噪点。
- Variance 小 → 噪声小 → 减小模糊，保留细节。
不过由于 filter 中对 variance 提前做了一遍高斯模糊来降低空域的噪声，一些阴影的细节会出现被模糊的情况。
阴影区域不会被模糊掉。
ASVGF
ASVGF 解决拖影和细节丢失问题，生成一张 temporal gradient 图，估计当前帧和历史帧的变化梯度。
asvgf_gradient_reproject.comp
-> 生成 gradient sample positions
path tracer
-> 对这些位置产生 gradient samples
asvgf_gradient_img.comp
-> 根据当前/历史亮度差生成 gradient image
asvgf_gradient_atrous.comp
-> 把稀疏 gradient 扩散成可用的低分辨率 gradient field
asvgf_temporal.comp
-> 读取 gradient，做 anti-lag
Temporal Accumulation & AntiLag
以 primary ray HF 为例，权重 alpha 受 temporal gradient 的变化影响：
if(temporal_sample_valid_diff)
{
// Compute the antilag factors based on the gradients
float antilag_alpha_hf = clamp(mix(1.0, global_ubo.flt_antilag_hf * grad_hf_spec.x, global_ubo.flt_temporal_hf), 0, 1);
// Adjust the history length, taking the antilag factors into account
// gradient大，hist_len_hf小，历史帧越容易被丢弃
float hist_len_hf = min(temporal_moments_histlen_hf.b * pow(1.0 - antilag_alpha_hf, 10) + 1.0, 256.0);
// Compute the blending weights based on history length, so that the filter
// converges faster. I.e. the first frame has weight of 1.0, the second frame 1/2, third 1/3 and so on.
float alpha_color_hf = max(global_ubo.flt_min_alpha_color_hf, 1.0 / hist_len_hf);
float alpha_moments_hf = max(global_ubo.flt_min_alpha_moments_hf, 1.0 / hist_len_hf);
// Adjust the blending factors, taking the antilag factors into account again
alpha_color_hf = mix(alpha_color_hf, 1.0, antilag_alpha_hf);
alpha_moments_hf = mix(alpha_moments_hf, 1.0, antilag_alpha_hf);
// Blend!
out_color_hf.rgb = mix(temporal_color_hf.rgb, color_curr_hf.rgb, alpha_color_hf);
out_moments_histlen_hf.rg = mix(temporal_moments_histlen_hf.rg, spatial_moments_hf.rg, alpha_moments_hf);
out_moments_histlen_hf.b = hist_len_hf;
}
q2rtx impl
在 G-Buffer 生成后，一个 compute shader pass reproject 到上一帧生成：
imageStore(IMG_ASVGF_GRAD_SMPL_POS_A, pos_grad, uvec4(gradient_idx));
imageStore(IMG_ASVGF_GRAD_HF_SPEC_PING, pos_grad, vec4(found_prev_lum, 0, 0));
imageStore(IMG_ASVGF_RNG_SEED_A, ipos, texelFetch(TEX_ASVGF_RNG_SEED_B, found_pos_prev, 0));
imageStore(IMG_PT_NORMAL_A, ipos, texelFetch(TEX_PT_NORMAL_B, found_pos_prev, 0));
imageStore(IMG_PT_BASE_COLOR_A, ipos, texelFetch(TEX_PT_BASE_COLOR_B, found_pos_prev, 0));
imageStore(IMG_PT_METALLIC_A, ipos, texelFetch(TEX_PT_METALLIC_B, found_pos_prev, 0));