From e5181f6061e2183b8e19009d8978bd54f89d5fcd Mon Sep 17 00:00:00 2001
From: neochaojin <neochaojin@tencent.com>
Date: Mon, 24 Feb 2025 16:53:23 +0800
Subject: [PATCH] =?UTF-8?q?=E6=94=B9=E8=BF=9B=E6=A0=BC=E5=BC=8F?=
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---
 docs/note/graphics/math/transform_04.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/docs/note/graphics/math/transform_04.md b/docs/note/graphics/math/transform_04.md
index adf4d12..9f68601 100644
--- a/docs/note/graphics/math/transform_04.md
+++ b/docs/note/graphics/math/transform_04.md
@@ -188,8 +188,11 @@ $$
 $$
 一般来说，当针对一个空间点做矩阵转换时，如果$\boldsymbol{M}[u_x, u_y, u_z, 1]^T=[v_x,v_y,v_z,1]^T$，那么
 $$
-(k\boldsymbol{M})[u_x, u_y, u_z, 1]^T=k[v_x,v_y,v_z,1]^T=[kv_x,kv_y,kv_z,k]^T=[v_x,v_y,v_z,1]^T
-$$
+\begin{aligned}
+(k\boldsymbol{M})[u_x, u_y, u_z, 1]^T&=k[v_x,v_y,v_z,1]^T\\
+&=[kv_x,kv_y,kv_z,k]^T\\
+&=[v_x,v_y,v_z,1]^T
+\end{aligned}$$
 可知$k\boldsymbol{M}$在针对空间点转换矩阵中等同于$\boldsymbol{M}$，所以为了表达方便，经常将上面得到的这个矩阵乘以-1，得到
 $$
 \boldsymbol{M}_{\text{persp}\rightarrow\text{ccv}}=\begin{bmatrix}