resolved

Renders what a spacecraft camera sees: Earth, Moon, Sun and a target spacecraft loaded from a mesh. The inputs are an epoch, the observer and target states and attitudes, and a camera model. Images are radiometric: the renderer works in radiance and converts to electrons and then DN through the camera's optics and detector, so an image should match what a real camera with the same parameters records.

It is built for closed-loop simulation, where each step renders one frame and latency is the number that matters. The goal is high-fidelity 1–2 MP RGB in about 10 ms end to end on a mid-range GPU (the reference is an RTX 3060); 100 ms is the ceiling, not the aim. Lighting fidelity and camera fidelity are selected separately, and each tier is benchmarked, so a run can trade fidelity for speed knowing what each step costs.

Rendering goes through wgpu. On a machine without a GPU, the same shaders run on the CPU through Mesa's lavapipe (vulkan-swrast on Arch, mesa-vulkan-drivers on Debian and Ubuntu).

Usage

use resolved::nalgebra::{UnitQuaternion, Vector3};
use resolved::radiometry::AU;
use resolved::{CameraConfig, Mesh, Pose, Renderer, Scene, Target};

# fn main() -> Result<(), resolved::Error> {
// Setup, once: device, shaders, spectra folded into the camera's channels.
let mut renderer = Renderer::new(CameraConfig::example(), Default::default())?;

// A model, uploaded once. Usually `Mesh::load(path, &options)` on an STL or glTF.
let stl = "solid panel
facet normal 0 0 0
outer loop
vertex 0 -1 -1
vertex 0 1 -1
vertex 0 0 1
endloop
endfacet
endsolid panel";
let panel = renderer.load_model(&Mesh::from_stl(&mut std::io::Cursor::new(stl), "panel", &Default::default())?)?;

// Each step: inertial positions in meters, Earth at the origin.
let target = Vector3::new(7.0e6, 0.0, 0.0);
let scene = Scene {
    earth_attitude: UnitQuaternion::identity(),
    sun: Vector3::new(AU, 0.0, 0.0),
    moon: Vector3::new(0.0, 3.844e8, 0.0),
    targets: vec![Target { model: panel, pose: Pose::new(target, UnitQuaternion::identity()) }],
};
let observer = Pose::looking_at(target + Vector3::new(20.0, 0.0, 0.0), target, Vector3::z()).unwrap();

let pending = renderer.submit(&scene, &observer)?;
// ... the simulation's own work for this step overlaps the GPU's here ...
let frame = pending.wait()?;

// 12-bit DN, channels r, g, b interleaved. The panel faces the Sun and the camera.
assert!(frame.dn_at(960, 540, 1) > 1000);
# Ok(())
# }

Command line

resolved render renders a scene described in TOML: camera overrides, the observer, Sun and Moon positions (or an epoch, with the astrokit feature) and targets with their models. examples/scenes/ has commented examples: boxwing.toml and boxwing-epoch.toml use a small generated model that is in the repository, and the rest use NASA spacecraft models (Landsat 8, ICESat-2, OSTM/Jason-2, an SSL-1300 and ACE) that scripts/fetch-models.sh downloads into examples/models/nasa/. That script needs Node (for npx), because most NASA models are Draco-compressed and are decoded on the way in.

cargo run --release -p resolved-cli -- render \
    --scene examples/scenes/boxwing.toml \
    --out frame.png --preview preview.png --timings 50

--out writes raw DN as a 16-bit PNG, shifted up to fill 16 bits as cameras usually store them: a 12-bit DN is the stored value divided by 16. It is linear, so it looks dark next to the preview.
--preview writes an 8-bit sRGB image for looking at, with the bias as black and saturation as white.
--timings N renders N frames and reports setup and per-frame times.
--adapter takes auto, gpu, cpu or part of an adapter's name; resolved adapters lists them.
--kernels DIR says where astrokit's kernels are, for scenes given by epoch. Without it the CLI tries ASTROKIT_KERNELS, then data/kernels, then the sibling ../astrokit checkout it was built against.

Features

astrokit: build a Scene from an epoch with astrokit's ephemerides (JPL DE440s for the Sun and Moon, ITRF93 for Earth's orientation), through ephemeris::Ephemeris. astrokit is not on crates.io, so this expects a checkout at ../astrokit next to this repository, and its kernels from its scripts/fetch-kernels.sh (or ASTROKIT_KERNELS pointing at them).

Status

M1 in progress: the library renders Earth, Moon, Sun and targets to DN through Renderer, and the CLI renders scene files; benchmarks are still to come. The work is tracked as Gitea milestones, each split into one issue per change:

Milestone	Scope
M1	Scene API, headless wgpu, analytic Earth/Moon/Sun, glTF and STL targets, Lambert shading with eclipse, pinhole camera, radiance to DN, CLI, benchmark baseline
M2	Self-shadowing, Earthshine and Moonshine, Earth and Moon albedo maps, Sun limb darkening
M3	Lens distortion, PSF, detector noise, saturation and blooming, stars
M4	BRDF and spectral materials, atmospheric scattering, clouds, lunar terrain
M5	Portable ray tracing, path tracing, label outputs for ML
M6	Validation against lunar, Earth and Mitsuba references; motion blur, rolling shutter, stray light; multi-band and 4k

Conventions

SI units throughout: meters, seconds, radians, watts.
Scene state is f64 in an inertial frame (GCRF). The GPU only sees f32 coordinates relative to the camera.

Layout

crates/resolved: the library.
crates/resolved-cli: the resolved command, for rendering without writing a host program.
examples/: scene files and the box-wing model (scripts/make-example-model.py regenerates it). The NASA scenes are generated by scripts/make-example-scenes.py.
scripts/: generators for compiled-in data and examples.
docs/optics.md: the optics and radiometry the renderer models, explained for readers without an optics background.

Development

Point git at the committed hooks once per clone:

git config core.hooksPath hooks

pre-commit runs formatting, clippy and the library unit tests; pre-push runs the full test suite, including doctests, and the documentation build.

The integration tests render on both a hardware GPU and lavapipe, so running them needs a Vulkan driver for the GPU and lavapipe installed. Set WGPU_BACKEND, WGPU_ADAPTER_NAME and the other standard wgpu environment variables to steer adapter discovery.

To see what the GPU tests render, set RESOLVED_TEST_IMAGES=1. Each test then writes a PNG per device to target/tmp/test-images/, scaled so a white surface facing the Sun is full white:

RESOLVED_TEST_IMAGES=1 cargo test --test bodies --test mesh

License

MIT

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