Stepping And Rendering

Stepping and Rendering

When to Use

Use this skill when the user asks to render a frame, step the renderer, simulate a sensor, or get an image from ovrtx.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Target API surface: Python, C/C++, or both.
• Application lifecycle stage: renderer creation, scene loading, stepping, warmup, output readback, or cleanup.
• Repository source snippets referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Read the relevant > **Source:** snippet before writing or explaining API usage.
• For code changes, preserve renderer lifecycle ordering and cleanup semantics for the selected language.

Instructions

1. Identify the requested language and lifecycle stage before choosing an example.
2. Read the referenced snippet that matches the requested stage and language.
3. Preserve the normal ovrtx order: create or initialize the renderer, load or compose USD, step or wait for work, read outputs when needed, then release C resources explicitly.
4. Apply the async, status-query, error-handling, and warmup skills when the workflow crosses those concerns.
5. When changing code, run the narrow example or docs test that owns the snippet whenever practical.

Output Format

• For explanations, cite the relevant API names, source snippets, and caveats.
• For code changes, summarize the files changed, snippets affected, and validation run.

Scripts

This skill has no scripts.

Limitations

• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

After loading USD content, you render frames by calling step(). Each step advances the simulation by delta_time and produces output for the specified render products (cameras/sensors). The result contains a hierarchy of products, frames, and render variables that can be mapped to access pixel data.

Simulation time vs. USD time

ovrtx tracks two independent clocks — a frequent point of confusion:

step() does not advance USD animation, and update_from_usd_time() does not advance the simulation/sensor clock. To render an animated frame at time t: call update_from_usd_time(t) first, then step(...). reset(time) resets simulation time only (not USD time, and distinct from reset_stage()). Remember update_from_usd_time takes seconds, not timecodes — see loading-usd for the timeCodesPerSecond conversion.

Step window vs. frame capture time

reset(time=T) re-bases the clock so the next step(dt) simulates [T, T + dt]; it does not emit a frame at T.

Python exposes both:

A default viewport camera captures instantaneously at the end of the step window, so its FrameOutput.start_time is T + dt, not T. That is expected, not a clock bug — assert products.simulation_start_time when checking the clock base.

Python

Single frame

Source: examples/python/minimal/main.py snippet step

Source (local-scene example): examples/python/projectors/main.py snippet projectors-render

Iterate over results

Source: examples/python/minimal/main.py snippet read-render-output

Render loop

Source: tests/docs/python/test_camera_sensors.py snippet doc-step-and-map-camera-outputs

Reset simulation time

Source: tests/docs/python/test_support_api.py snippet doc-reset-async

renderer.reset(time=0.0) resets accumulated simulation time (distinct from reset_stage()).

Source: tests/docs/python/test_camera_sensors.py snippet doc-reset-simulation-clock

Assert the clock base via products.simulation_start_time after the next step (distinct from FrameOutput.start_time).

C

Step, wait, fetch

Source: examples/c/minimal/main.cpp snippet step-renderer

Source (continued): examples/c/minimal/main.cpp snippet fetch-results

Iterate over C results

Source: examples/c/minimal/main.cpp snippet find-output-helper

Key Types / Functions

Result hierarchy:

RenderProductSetOutputs
  .simulation_start_time / .simulation_end_time   # step window [t, t+dt]
  -> ProductOutput (one per render product path)
    -> FrameOutput (one per frame produced during the step)
      .start_time / .end_time                     # sensor capture inside the window
      -> RenderVarOutput (one per full RenderVar prim path: /Render/Camera/LdrColor, /Render/Camera/HdrColor, etc.)

Troubleshooting

• The first step from a newly built application will block for 1-2 minutes while shaders are compiled and cached. Wait at least 5 minutes before treating this as a failure; later runs should be faster once the shader cache is populated.
• In C, you must call ovrtx_destroy_results() after processing to free resources. ovrtx will warn if results are leaked.
• delta_time controls sensor simulation timing. A camera without motion blur produces one frame per step regardless of delta.
• The render product paths must match actual RenderProduct prims in the USD stage.
• In Python, RenderProductSetOutputs auto-destroys on garbage collection. Step result metadata is released when products falls out of scope; live render-var mappings and their DLPack views are unaffected (they have their own independent lifetime).

In Attached Mode (ovrtx 0.4+)

When ovrtx is attached to ovstage, step through ovrtx_step_with_stage(renderer, products, delta_time, ordinal, ...) (Python: renderer.step(products, dt, ordinal=n)). The ordinal is a committed-publication gate — the renderer observes ovstage state at or above that ordinal. Call ovrtx_update_from_stage(ordinal) first to pull committed prim/attribute state into the renderer, then step. See docs/core/ovstage_integration.rst "Ordinals and Write-Floor Gates" for the ordinal model.

See docs/core/ovstage_integration.rst, skills/update-0_3-0_4-c/SKILL.md and skills/update-0_3-0_4-python/SKILL.md.

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.