mlsanityData to 7 Oct 2026

OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding

arXiv 2103.07679 ↗First posted 13 Mar 2021cs.CL cs.CVcited by 15

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Abstract (arXiv)

To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects. However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded due to space utilization issues. In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects. OCID-Ref consists of 305,694 referring expressions from 2,300 scenes with providing RGB image and point cloud inputs. To resolve challenging occlusion issues, we argue that it's crucial to take advantage of both 2D and 3D signals to resolve challenging occlusion issues. Our experimental results demonstrate the effectiveness of aggregating 2D and 3D signals but referring to occluded objects still remains challenging for the modern visual grounding systems. OCID-Ref is publicly available at https://github.com/lluma/OCID-Ref

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 13 Mar 2021.

Builds on

11 of 17 references.

References · 11

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  3. 2016Modeling Context in Referring Expressionscited by 1,054
  4. 2015Generation and Comprehension of Unambiguous Object Descriptionscited by 897
  5. 2019ScanRefer: 3D Object Localization in RGB-D Scans using Natural Languagecited by 446
  6. 2019Dynamic Graph Attention for Referring Expression Comprehensioncited by 111
  7. 2019EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasetscited by 58
  8. 2020Graph-Structured Referring Expression Reasoning in The Wildcited by 58
  9. 2018Interactive Visual Grounding of Referring Expressions for Human-Robot Interactioncited by 55
  10. 2020Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehensioncited by 50
  11. 2020GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detectioncited by 8

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Citing papers per year: 2022: 1; 2023: 3; 2024: 7; 2025: 3; 2026: 1.

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  1. 2024Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Modelscited by 336
  2. 2023Language-Driven Representation Learning for Roboticscited by 163
  3. 2022ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehensioncited by 119
  4. 2024Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Accelerationcited by 40
  5. 2024Learning Manipulation by Predicting Interactioncited by 36
  6. 2024SUGAR: Pre-training 3D Visual Representations for Roboticscited by 26
  7. 2023Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Cluttercited by 22
  8. 2024Locality Alignment Improves Vision-Language Modelscited by 13
  9. 2024Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Controlcited by 11
  10. 2025Mind to Hand: Purposeful Robotic Control via Embodied Reasoningcited by 7
  11. 20247B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancementcited by 5
  12. 2023GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulationcited by 4
3 more
  1. 2025Open-Source Multimodal Moxin Models with Moxin-VLM and Moxin-VLAcited by 4
  2. 2025Should VLMs be Pre-trained with Image Data?cited by 1
  3. 2026Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoderscited by 0