# SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences

arXiv: https://arxiv.org/abs/2102.10820 · HTML: https://mlsanity.com/paper/2102.10820/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes](https://mlsanity.com/paper/1903.01568/) (2019)
- [EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets](https://mlsanity.com/paper/1902.01626/) (2019)
- [Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer](https://mlsanity.com/paper/1511.03240/) (2015)
- [3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams](https://mlsanity.com/paper/1905.00525/) (2019)
- [LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes](https://mlsanity.com/paper/1707.04796/) (2017)
- [LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking](https://mlsanity.com/paper/1904.09085/) (2019)
- [Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation](https://mlsanity.com/paper/1807.06072/) (2018)
- [YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation](https://mlsanity.com/paper/2004.11657/) (2020)

## Built on by (most cited first, 10 of 10)

- [360^circ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation](https://mlsanity.com/paper/2309.06197/) (2023)
- [TICaM: A Time-of-flight In-car Cabin Monitoring Dataset](https://mlsanity.com/paper/2103.11719/) (2021)
- [TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera](https://mlsanity.com/paper/2108.12196/) (2021)
- [Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps](https://mlsanity.com/paper/2209.12744/) (2022)
- [ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception](https://mlsanity.com/paper/2203.00283/) (2022)
- [Text3DAug -- Prompted Instance Augmentation for LiDAR Perception](https://mlsanity.com/paper/2408.14253/) (2024)
- [Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses](https://mlsanity.com/paper/2201.07665/) (2022)
- [DeepScanner: a Robotic System for Automated 2D Object Dataset Collection with Annotations](https://mlsanity.com/paper/2108.02555/) (2021)
- [3D Annotation Of Arbitrary Objects In The Wild](https://mlsanity.com/paper/2109.07165/) (2021)
- [Adding Another Dimension to Image-based Animal Detection](https://mlsanity.com/paper/2604.09210/) (2026)

## Abstract (arXiv)

Large labeled data sets are one of the essential basics of modern deep learning techniques. Therefore, there is an increasing need for tools that allow to label large amounts of data as intuitively as possible. In this paper, we introduce SALT, a tool to semi-automatically annotate RGB-D video sequences to generate 3D bounding boxes for full six Degrees of Freedom (DoF) object poses, as well as pixel-level instance segmentation masks for both RGB and depth. Besides bounding box propagation through various interpolation techniques, as well as algorithmically guided instance segmentation, our pipeline also provides built-in pre-processing functionalities to facilitate the data set creation process. By making full use of SALT, annotation time can be reduced by a factor of up to 33.95 for bounding box creation and 8.55 for RGB segmentation without compromising the quality of the automatically generated ground truth.