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Volt

Volt is the official Python implementation of a Transformer model for 3D scene understanding. It partitions a scene into non-overlapping volumetric patches, embeds them as tokens, processes them with global attention, and maps upsampled voxel-resolution features to semantic predictions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Volt — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Volt when working on 3D scene semantic prediction and you need an implementation with documented preprocessing and training paths for named research datasets.

Who it is for

  • Researchers developing or evaluating 3D scene semantic-segmentation methods.
  • Developers working with voxel-based scene understanding and able to prepare datasets and run Python training workflows.

Documented uses

  • Explore the implementation of a Transformer encoder for volumetric scene tokens.
  • Prepare supported 3D datasets using repository-specific preprocessing scripts.
  • Set up the project environment and follow the documented semantic-segmentation training configurations.
  • Inspect adjacent code references for instance segmentation or image-feature integration with 3D backbones.
Before you choose

Requirements & limits

The public GitHub repository identifies an MIT license. Setup instructions specify CUDA 12.6 and recommend uv for environment management.

  • The model’s documented output is semantic prediction at voxel resolution.
  • Repository workflows include multiple datasets, but their preprocessing requirements differ.
  • The ScanNet++ workflow uses a supplied updated mapping because the original benchmark mapping was found not to cover all labels.
  • For Waymo, preprocessing uses only TOP LiDAR points because the README says those points have semantic labels.
  • Instance-segmentation components are incorporated from SGIFormer; image-feature integration with 3D backbones is pointed to a separate DITR codebase.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Preprocess ScanNet++ for Volt

Run the repository’s ScanNet++ preprocessing script with its updated benchmark label mapping.

Before you start

  • Volt repository and its prepared Python environment
  • Raw ScanNet++ dataset directory
  • Output directory for processed ScanNet++ data

Steps

  1. Set SCANNETPP_DIR to the dataset root and PROCESSED_SCANNETPP_DIR to the desired output root.

  2. Run the command below from the repository.

    Documented command
    python pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py --dataset_root ${SCANNETPP_DIR} --output_root ${PROCESSED_SCANNETPP_DIR}

Expected outcome

The repository’s ScanNet++ preprocessing workflow is invoked using its supplied updated mapping file.

Guide limitations

  • The README reports that the original benchmark mapping was not exhaustive and that some labels had mapped to ignore.
  • The steps do not cover downloading the dataset or training a model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
YilmazKadir/Volt · Official homepage title · retained in its source language
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