ZVF and Neuroglancer precomputed

Terms

Neuroglancer precomputed

A family of static binary formats for serving volumetric image data, annotation data, skeleton data, and mesh data to the Neuroglancer 3-D viewer. Data is stored as a directory of binary chunk files served over HTTP. Defined by Google at https://github.com/google/neuroglancer/tree/master/src/datasource/precomputed.

Precomputed annotations

A precomputed sub-format for point, line, axis-aligned bounding box, and ellipsoid annotations. Each annotation is a small fixed-size binary record. Annotations are stored in spatial chunks indexed by a regular 3-D grid.

Precomputed skeletons

A precomputed sub-format for neuronal morphology skeletons. Each skeleton object is a binary blob containing vertex positions and edge pairs, stored in a spatially indexed shard file.

Precomputed meshes

A precomputed sub-format for triangulated surface meshes. Stored as draco-compressed or raw binary per-fragment files, indexed via a manifest JSON. Two sub-formats exist: the legacy per-fragment format and the newer sharded mesh format.

info file

The JSON metadata file at the root of a Neuroglancer precomputed layer that declares the data type, scales, voxel resolution, chunk sizes, and layer-specific configuration. Analogous to ZVF’s root .zattrs.

Introduction

ZVF and Neuroglancer precomputed share the same fundamental goal — spatially indexed, multi-resolution storage of 3-D geometry data — but were designed for different constraints. Precomputed is a read-only static serving format: data is written once and served over HTTP to Neuroglancer clients. ZVF is a read-write cloud-native format: data can be read and written from any Zarr-compatible environment, and the format is not tied to any specific viewer.

Understanding the relationship between the two formats is important for users of zv-ngtools, which translates ZVF stores into Neuroglancer layers and optionally exports them to precomputed format for static hosting.

Technical reference

Annotation data

Precomputed annotation format

Precomputed annotations support four geometric primitives: point, line, axis_aligned_bounding_box, and ellipsoid. Each annotation is serialised as a compact fixed-size binary record:

  • Point: 12 bytes (three float32 x,y,z).

  • Line: 24 bytes (two point3).

  • Bounding box: 24 bytes (min point3 + max point3).

  • Ellipsoid: 36 bytes (centre point3 + radii point3 + angles point3).

Annotations are stored in spatial chunks; the chunk grid is declared in the info file. Multiple spatial scales (levels of detail) are supported via separate scale entries in info.

Precomputed annotations do not support:

  • Ordered paths (no polyline or streamline type).

  • Per-annotation scalar attributes (only id, type, and geometry).

  • Discrete object model with per-object retrieval by ID.

ZVF equivalent

Precomputed type

Closest ZVF type

Notes

point

point_cloud

ZVF adds per-vertex attributes and multi-resolution

line

line

ZVF adds per-vertex attributes

axis_aligned_bounding_box

mesh (degenerate box)

No native ZVF bbox type; store as 8-vertex mesh

ellipsoid

parametric/ group

Stored as parameter tuple; no native render in ZVF viewers

ZVF is strictly richer than precomputed annotations: all precomputed annotation types can be represented in ZVF (with some loss of compactness), but not vice versa (streamlines, graphs, and skeletons have no precomputed annotation equivalent).

zv-ngtools translation

zv-ngtools serves ZVF point clouds and line stores as Neuroglancer annotation layers by translating VG slices to annotation chunk binaries on the fly:

from ngtools.local.viewer import LocalNeuroglancer

viewer = LocalNeuroglancer()
viewer.add("points.zarrvectors")   # served as Neuroglancer point annotations

Skeleton data

Precomputed skeleton format

Precomputed skeletons are stored in the skeletons/ sub-directory. Each skeleton object has a binary file containing:

  • A header with vertex count and edge count.

  • A (n_vertices, 3) float32 vertex position array.

  • A (n_edges, 2) uint32 edge array (local vertex index pairs).

No spatial indexing: to retrieve one skeleton, fetch its binary file directly. Spatial queries require fetching all skeleton files in a region.

Precomputed skeletons also support a sharded format (sharding_index + shard files) that packs many skeletons into fewer files for cloud efficiency.

Attribute support: precomputed skeletons support per-vertex vertex_attributes (float scalars and float vectors) stored alongside vertex positions.

ZVF skeleton vs precomputed skeleton

Property

ZVF skeleton

Precomputed skeleton

Spatial index

Yes (VG index + chunk grid)

No (direct file per object)

Multi-resolution

Yes

No

Per-vertex attributes

Yes (arbitrary dtypes)

Yes (float32 only)

Per-object attributes

Yes

No

SWC compatibility

Yes (swc_type, radius)

No

Cloud efficiency

Chunk-level parallelism

Shard-level parallelism

Write API

Yes

No (static format)

Viewer support

Via zv-ngtools

Native Neuroglancer

Export to precomputed skeletons

The precomputed-skeletons exporter (and its CLI subcommand) lives in the companion package zarr-vectors-tools. It translates a ZVF skeleton store to the Neuroglancer sharded skeleton format, writing one shard file per chunk. The output can be served directly by any HTTP server; add the layer URL to Neuroglancer as a skeletons data source.

Mesh data

Precomputed mesh formats

Two precomputed mesh formats exist:

Legacy per-fragment format: One binary file per mesh object, stored at meshes/<object_id>:0. Each file contains:

  • A (n_vertices, 3) float32 position array.

  • A (n_faces * 3,) uint32 face array (no face structure; flat triplets).

Sharded mesh format: Meshes are split into spatial fragments, each fragment covering one spatial chunk. Fragments are packed into shard files. Draco compression is supported. A per-object manifest JSON declares which fragment shards contain each object.

ZVF mesh vs precomputed mesh

Property

ZVF mesh

Precomputed mesh (sharded)

Spatial indexing

Chunk + VG index

Fragment grid

Draco compression

Optional

Supported

Multi-resolution

Yes

No (single resolution)

Per-vertex attributes

Yes

No

Write API

Yes

No

Viewer support

Via zv-ngtools

Native Neuroglancer

The ZVF mesh fragment model (faces assigned to chunks by centroid) is intentionally similar to the Neuroglancer sharded fragment model. The precomputed_export tool in zv-ngtools exploits this similarity to produce precomputed shard files directly from ZVF chunk data with minimal transformation.

Export to precomputed meshes

The precomputed-meshes exporter (and its CLI subcommand) lives in the companion package zarr-vectors-tools.

Summary: choosing between ZVF and precomputed

Use case

ZVF

Precomputed

Cloud-native read-write pipeline

✓

✗

Static Neuroglancer serving (no write needed)

Via export

✓

Multi-resolution with object sparsity

✓

✗

Per-vertex float attributes (non-float32)

✓

✗

Streamlines / tractography

✓

✗

Native Neuroglancer support (no extra tools)

Via zv-ngtools

✓

Arbitrary query API (Python, Julia, R)

✓

✗

For most new projects that originate data computationally, ZVF is the primary format. Precomputed export is a one-way publication step for sharing with Neuroglancer users who do not have zv-ngtools installed.