# Preview and render > Render a scenario as a 2D top-down video, a schematic view or photoreal 3D sensor data, on your machine or in the cloud. Source: https://simforge.ai/docs/render One renderer, the Helios Engine, in three modes: | Mode | What you get | Runs on | |---|---|---| | `2d` | Top-down map video of the scenario | CPU | | `schematic` | A clean schematic view through the rig's cameras | GPU | | `3d` | Photoreal camera images and sensor passes | GPU | ## Pick a sensor rig Schematic and 3D renders look through a rig: the cameras on the vehicle. Without `--rig` they use the `basic-dash-camera` rig on the ego, a front camera at the preset's capture (1280x720 at 24 fps for the default `showcase`); write your own to choose others. ```sh simforge rig init --preset basic-dash-camera --out rig.json simforge rig show rig.json ``` `rig init` writes the cameras at the `--profile` capture: `showcase` (1280x720 at 24 fps) unless you pass `--profile training` (512x384 at 20 fps). The basic dash camera's camera is `dashcam-front`. Other presets include `alpamayo-2cam`, `alpamayo-6cam`, `tesla-hw3` and `waymo-5th-gen`; `simforge rig init --help` lists them all, and the vehicle classes. ## Preview a frame ```sh simforge preview frame s.json --mode 2d --at 5 --out plan.png simforge preview frame s.json --mode 3d --rig rig.json --camera dashcam-front --at 5 --out frame.png simforge preview clip s.json --mode 3d --rig rig.json --camera dashcam-front --at 5 --seconds 3 --out clip.mp4 ``` ## Render ```sh simforge render s.json --mode 2d --out out-2d simforge render s.json --mode schematic --rig rig.json --out out-schematic simforge render s.json --mode 3d --rig rig.json --out out-3d ``` `--out` is a folder that must not exist or be empty. 3D renders take `--preset showcase` or `--preset training` (the dataset look at a fixed cost per frame); without `--preset`, a 3D render uses `showcase`, locally and with `--cloud`. They also take `--passes` to choose among `rgb`, `id`, `depth` and `semantic`. ## GPU memory and texture tiers How much GPU memory a 3D render needs depends on the map and the texture tier much more than on the cameras. The CLI picks the tier from your GPU: full textures (`uastc-full`) on 16 GiB and more, 512 px textures (`bc7-512`) below that, and says so in the result (`localDefaults`). `--textures uastc-full` or `--textures bc7-512` overrides it. On an Apple silicon Mac the GPU memory is three quarters of the Mac's memory: a 24 GB Mac gets full textures, a 16 GB Mac 512 px textures. | GPU memory | What fits (measured peak) | |---|---| | 8 GiB | 512 px textures on every public map, up to six 1920x1080 cameras (4.5 GiB) | | 12 GiB | 512 px textures everywhere; full textures on every public map except san-ramon-phase-1, one 1280x720 camera (8.1 GiB) | | 16 GiB | Full textures on every public map, up to six 1920x1080 cameras (11.9 GiB) | Peak GPU memory of one 3D render (showcase preset, one 1280x720 camera unless stated): | Map | Full textures | 512 px textures | |---|---|---| | san-ramon-phase-1 | 11.3 GiB | 3.8 GiB | | san-ramon-phase-1, six 1920x1080 cameras | 11.9 GiB | 4.5 GiB | | di-rosa-sf | 8.1 GiB | 2.8 GiB | | richmond-field-station | 7.3 GiB | 2.1 GiB | | belmont-research-center | 7.2 GiB | 2.3 GiB | | yale-street | 6.5 GiB | 2.3 GiB | | el-camino-road | 3.5 GiB | 1.9 GiB | | easterbrook-discovery-school | 2.0 GiB | 1.8 GiB | The training preset needs about the same; a schematic render needs 0.2 GiB. Measured on an RTX 5080 (driver 595.84). The machine-readable table is `sdk/contracts/runtime-support.v1.json` in the toolkit. ## Output format A 3D render writes: | Path | Format | |---|---| | `render.json` | The render's summary, also printed by the command: preset, rig, texture tier, map and actor-pack identities, gates, timings, videos, warnings | | `results.json` | Every captured file with its sensor, tick, pass, byte count and sha256 | | `/.rgb.png` | The camera image, RGBA8 PNG, sRGB | | `/.id.png` | Instance ids, RGBA8 PNG: id = R + 256·G + 65536·B, 0 = sky or background | | `legend.json` | Instance id to the static map mesh it is | | `/.semantic.png` | Semantic class in the B channel of an RGBA8 PNG, CARLA CityScapes ids: 0 unlabeled, 1 building, 2 fence, 4 pedestrian, 5 pole, 7 road, 9 vegetation, 10 vehicle, 12 traffic sign, 18 traffic light | | `/.depth.f32.bin` | Raw little-endian float32, width x height, row-major, reverse-Z: distance along the camera axis in metres = `nearM / value` (`nearM` is the camera's near plane, 0.05 m by default); 0 = nothing hit | | `observed-frames.jsonl` | One line per frame: every actor's position, rotation and visibility as rendered | | `parity.json` | How closely the rendered poses match the simulated trace | | `video/.mp4` | The RGB frames as H.264 video (needs `ffmpeg`; `--video off` skips it) | | `job/` | The exact inputs the renderer ran with (timeline, scene state, texture plan) | Ticks count from the clip start at the rig's frame rate. Lidar sources write `/.ply` and radar sources `.csv`. A 2D render writes `render.json` and `video/topdown.mp4`. Traces are identical on every machine; pixels are identical run to run on the same GPU model and driver, and differ slightly between GPUs. Without ffmpeg, a 2D render that requests video preserves its images under `frames/topdown/.png` and reports that directory in `frame.directory`; it warns that no video was encoded. `--video off` remains a render-and-measure operation without retained frames. ## Before you render Check the GPU with `simforge doctor` and the runtime's render modes with `simforge runtime status`. A render that cannot run on this machine fails with a named error; it never falls back to another device or to the cloud. A scene that does not fit in GPU memory fails with `native_gpu_out_of_memory`: use `--textures bc7-512`, fewer or smaller cameras, or `--cloud`. A 3D render whose GPU memory cannot be measured warns `gpu_memory_unmeasured`; `--vram-budget` sets the budget explicitly. ## Render in the cloud The same command runs on SimForge's machines with `--cloud` and a cost cap in cents: ```sh simforge render s.json --mode 3d --rig rig.json --out out-3d --cloud --max-cost 200 --wait --download ``` Cloud downloads include every captured sensor file named by `results.json`, the videos and output metadata. The local renderer's diagnostic `job/` directory is not exported in full; its authoritative `job/timeline.json` is included. See [Run in the cloud](/docs/cloud) for pricing, caps and following a run.