# EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets

arXiv: https://arxiv.org/abs/1902.01626 · HTML: https://mlsanity.com/paper/1902.01626/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 30 of 58)

- [High-Quality Entity Segmentation](https://mlsanity.com/paper/2211.05776/) (2022)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling](https://mlsanity.com/paper/2109.11103/) (2021)
- [Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances](https://mlsanity.com/paper/2108.04145/) (2021)
- [Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation](https://mlsanity.com/paper/2312.03502/) (2023)
- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Mean Shift Mask Transformer for Unseen Object Instance Segmentation](https://mlsanity.com/paper/2211.11679/) (2022)
- [ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation](https://mlsanity.com/paper/2108.11771/) (2021)
- [Unknown Object Segmentation from Stereo Images](https://mlsanity.com/paper/2103.06796/) (2021)
- [OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding](https://mlsanity.com/paper/2103.07679/) (2021)
- [RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks](https://mlsanity.com/paper/2106.15711/) (2021)
- [Towards Open-World Grasping with Large Vision-Language Models](https://mlsanity.com/paper/2406.18722/) (2024)
- [High-Quality Entity Segmentation and Grounding](https://mlsanity.com/paper/2402.02555/) (2024)
- [Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition](https://mlsanity.com/paper/2210.00843/) (2022)
- [AdaTerm: Adaptive T-Distribution Estimated Robust Moments for Noise-Robust Stochastic Gradient Optimization](https://mlsanity.com/paper/2201.06714/) (2022)
- [SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences](https://mlsanity.com/paper/2102.10820/) (2021)
- [Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction](https://mlsanity.com/paper/2302.03793/) (2023)
- [Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation](https://mlsanity.com/paper/2204.09847/) (2022)
- [FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments](https://mlsanity.com/paper/2207.03333/) (2022)
- [GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes](https://mlsanity.com/paper/2504.06866/) (2025)
- [Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps](https://mlsanity.com/paper/2209.12744/) (2022)
- [A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment](https://mlsanity.com/paper/2302.06301/) (2023)
- [ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception](https://mlsanity.com/paper/2203.00283/) (2022)
- [OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad](https://mlsanity.com/paper/2503.18695/) (2025)
- [Persistent Homology Meets Object Unity: Object Recognition in Clutter](https://mlsanity.com/paper/2305.03815/) (2023)
- [ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models](https://mlsanity.com/paper/2502.03266/) (2025)
- [SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments](https://mlsanity.com/paper/2212.11922/) (2022)
- [Rapid Image Labeling via Neuro-Symbolic Learning](https://mlsanity.com/paper/2306.10490/) (2023)
- [Robot Instance Segmentation with Few Annotations for Grasping](https://mlsanity.com/paper/2407.01302/) (2024)
- [Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks](https://mlsanity.com/paper/2111.11114/) (2021)

## Abstract (arXiv)

Developing robot perception systems for recognizing objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms. This paper presents the EasyLabel tool for easily acquiring high quality ground truth annotation of objects at the pixel-level in densely cluttered scenes. In a semi-automatic process, complex scenes are incrementally built and EasyLabel exploits depth change to extract precise object masks at each step. We use this tool to generate the Object Cluttered Indoor Dataset (OCID) that captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. OCID is used to perform a systematic comparison of existing object segmentation methods. The baseline comparison supports the need for pixel- and object-wise annotation to progress robot vision towards realistic applications. This insight reveals the usefulness of EasyLabel and OCID to better understand the challenges that robots face in the real-world. Copyright 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.