Tangent-Space Normal Mapping Normalization

Matthias Moulin · 2018-04-26T11:31:15

An extension of the Sponza model adds 13 tangent-space normal maps (now every submodel has a normal map). Though, these additional normal maps seem far from normalized. Luckily, the z channel is provided as well. So I decided to normalize them myself (I converted all .tga files first to .png files): import cv2 import numpy as np def normalize(v): norm = np.linalg.norm(v) return v if norm == 0.0 else v / norm def normalize_image(fname): # [0, 255]^3 img_in = cv2.imread(fname) img_out = np.zeros(img_in.shape) # [0.0, 1.0]^3 img_in = img_in.astype(np.float64) / 255.0 # [-1.0, 1.0]x[-1.0, 1.0]x[0.0, 1.0] img_in[:,:,:2] = 2.0 * img_in[:,:,:2] - 1.0 height, width, _ = img_in.shape for i in range(0,height): for j in range(0,width): # [-1.0, 1.0]x[-1.0, 1.0]x[0.0, 1.0] v = np.float64(normalize(img_in[i,j])) # [0.0, 1.0]^3 v[:2] = 0.5 * v[:2] + 0.5 # [0, 255]^3 img_out[i,j] = v * 255.0 cv2.imwrite(fname, img_out) This works ok for the original tangent-space normal maps, but not for the additional 13 tangent-space normal maps. Way too dark original: Not so very blue-ish normalized version: Any ideas? Another example: