Introduction

In computer graphics and rendering, image-based lighting (IBL) is a technique used to simulate realistic lighting environments by using high dynamic range (HDR) images. This technique has gained popularity due to its ability to create stunning and realistic lighting effects in computer-generated scenes.

In this blog post, we will explore how to implement image-based lighting in C++. We will discuss the basic concepts of image-based lighting and walk through the steps to implement it in a C++ application.

Prerequisites

To follow along with this tutorial, you should have a basic understanding of C++ programming and computer graphics concepts. It is also helpful to have knowledge of shaders and rendering techniques.

The Basics of Image-Based Lighting

Image-based lighting relies on the use of a 2D environment map, typically in HDR format, to simulate the lighting conditions of a real-world environment. The environment map captures the lighting information from all directions and is used to illuminate the scene.

The main steps involved in implementing image-based lighting are as follows:

1. Load the HDR environment map.
2. Convert the environment map to a cube map.
3. Generate a set of spherical harmonics coefficients from the cube map.
4. Evaluate the lighting in the fragment shader using the spherical harmonics coefficients.

We will now dive into each of these steps and see how they can be implemented in C++.

Step 1: Load the HDR Environment Map

To load the HDR environment map, we can use a library like stb_image. This library provides a simple and efficient way to load various image formats in C++. Here’s an example code snippet that shows how to load an HDR image using stb_image:

#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"

int width, height, channels;
float* hdrData = stbi_loadf("environment.hdr", &width, &height, &channels, 0);

Make sure to replace “environment.hdr” with the actual path to your HDR environment map.

Step 2: Convert the Environment Map to a Cube Map

To convert the HDR environment map into a cube map, we need to project the 2D image onto a cube. This cube will represent the six faces of the cube map.

There are several ways to perform this conversion, but one common approach is to use a process called “cubemap rendering”. This involves rendering a scene with a camera at the center of the cube, capturing the environment map by rendering each face of the cube and storing the results in a texture.

The implementation of the cubemap rendering process is beyond the scope of this blog post, but libraries like OpenGL or Vulkan provide APIs to achieve this.

Step 3: Generate Spherical Harmonics Coefficients

Spherical harmonics (SH) are a set of mathematical functions that can represent spherical data, such as lighting information from the environment map. To calculate the set of spherical harmonics coefficients from the cube map, we can use the technique known as “spherical harmonics irradiance”.

This involves sampling the cube map at various points on a unit sphere and computing the average lighting value around each point. The resulting values are used to compute the spherical harmonics coefficients.

The implementation of spherical harmonics irradiance is complex and requires advanced mathematical computations. Libraries like AMD Radeon GPU Pro or NVIDIA NVAPI provide functions to calculate spherical harmonics coefficients.

Step 4: Evaluate Lighting in the Fragment Shader

Once we have the spherical harmonics coefficients, we can evaluate the lighting in the fragment shader of our rendering pipeline. The fragment shader takes the surface normal, material properties, and other inputs as parameters and computes the final lighting value based on the spherical harmonics coefficients.

Here’s an example code snippet that shows how to evaluate the lighting in the fragment shader using spherical harmonics coefficients:

#version 330 core
in vec3 FragPos;
in vec3 Normal;
out vec4 FragColor;

uniform float shCoeffs[9]; // Spherical harmonics coefficients

void main()
{
    // Evaluate lighting using SH coefficients
    vec3 SHLighting = vec3(0.0);
    for (int i = 0; i < 9; i++)
    {
        SHLighting += shCoeffs[i] * SH_Basis(i, Normal);
    }

    // Compute final color
    FragColor = vec4(SHLighting, 1.0);
}

Replace SH_Basis with the appropriate spherical harmonics basis function.

Conclusion

Image-based lighting is a powerful technique that can greatly enhance the realism of computer-generated scenes. By simulating the lighting conditions of real-world environments, image-based lighting allows for more immersive and visually appealing graphics.

In this blog post, we explored the basics of image-based lighting and discussed the steps involved in implementing it in a C++ application. We covered loading the HDR environment map, converting it to a cube map, generating spherical harmonics coefficients, and evaluating the lighting in the fragment shader.

By following these steps and understanding the underlying concepts, you can harness the power of image-based lighting to create stunning and realistic graphics in your C++ applications.

#tech #C++ #imagebasedlighting