Cornell Box Raytracer

A pure Python raytracer that renders the Cornell Box scene with:

Reflections - mirror/chrome spheres reflect surrounding geometry
Caustics & Refraction - glass spheres bend light (dielectric material with Schlick approximation)
Multiple Bounces - recursive path tracing up to configurable depth
Next Event Estimation (NEE) - explicit light sampling for fast convergence
Anti-aliasing - supersampled pixels with jittered rays
Multiprocessing - parallel rendering across CPU cores

Requirements

Python 3.8+
numpy
Pillow
rich (progress bars, colored tables)
matplotlib (render metrics graphs)

pip install numpy Pillow rich matplotlib

Features

Rich Progress Bar - animated progress with ETA, speed, and row count
Scene Configuration Table - formatted parameter display
Region Brightness Analysis - color-coded brightness per region (left/right wall, floor, ceiling, center)
Render Metrics Graph - saved as render_metrics.png, shows brightness convergence and render preview
Live Preview - preview.png updated during rendering
Reflections - mirror/chrome spheres reflect surrounding geometry
Caustics & Refraction - glass spheres bend light (dielectric material with Schlick approximation)
Multiple Bounces - recursive path tracing up to configurable depth
Next Event Estimation (NEE) - explicit light sampling for fast convergence
Anti-aliasing - supersampled pixels with jittered rays
Multiprocessing - parallel rendering across CPU cores

Usage

python raytracer.py [width] [height] [samples_per_pixel] [max_bounces] [output]

Examples

# Quick preview (4 min)
python raytracer.py 200 150 20 10 preview.png

# Good quality (~10 min)
python raytracer.py 400 300 50 15 cornell.png

# High quality (~40 min)
python raytracer.py 800 600 100 15 cornell_hq.png

# Custom camera and scene parameters can be set by editing the __main__ block

Parameters

Parameter	Default	Description
width	400	Image width in pixels
height	300	Image height in pixels
samples_per_pixel	50	Anti-aliasing samples per pixel
max_bounces	15	Maximum ray bounce depth
output	cornell_box.png	Output PNG filename

Workers

Multiprocessing uses 4 workers by default. Change workers=4 in the render() call.

Scene Description

The scene is the classic Cornell Box:

5 walls (floor, ceiling, left=red, right=green, back=white) with the front face open
A small bright light source on the left wall
A glass sphere (refractive, IOR=1.5) - demonstrates refraction and caustics
A mirror sphere (perfect reflection) - demonstrates reflections
A red diffuse sphere - demonstrates diffuse scattering

Camera

Position: (278, 278, -800)
Look-at: (278, 278, 278) (box center)
FOV: 40°
Focus distance: 1078

Materials

Material	Implementation
Diffuse (Lambertian)	Cosine-distributed random scattering + NEE
Mirror	Perfect specular reflection
Dielectric	Snell refraction + Schlick fresnel + random bounce
Light	Emissive surface, sampled via NEE

Progress Indicators

The renderer uses Rich for terminal UI and matplotlib for metrics graphs:

Progress Bar - animated with spinner, bar, percentage, ETA, and row count
Settings Table - formatted parameter display with colored values
Region Brightness - color-coded ASCII bar chart showing brightness per region
Metrics Graph - saved as render_metrics.png, shows brightness convergence curve and live render preview
Preview Image - saved as preview.png and updated during rendering

Performance

Measured on 4 workers, single machine:

Resolution	spp	Time
200x150	20	~40s
400x300	50	~10 min
800x600	50	~40 min

How It Works

Primary rays are cast from the camera through each pixel (with supersampling jitter)
Ray-object intersection tests against planes (walls), spheres, and boxes (light)
Surface scattering:
- Diffuse: random hemisphere direction + explicit light sampling (NEE)
- Mirror: perfect reflection vector
- Dielectric: refraction with Snell's law, Schlick approximation for total internal reflection
Recursive bouncing: each scattered ray continues tracing up to max_bounces
Radiance estimation: L = Le + ∫ f_r * L_i * cos θ dω approximated via Monte Carlo integration

Next Event Estimation

For diffuse surfaces, the direct light contribution is computed by explicitly sampling points on the light source, testing visibility via shadow rays, and weighting by the BRDF and light PDF. This dramatically reduces variance compared to pure path tracing.

Output

Renders save as PNG with gamma correction (sqrt/gamma 2.0 applied).

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