warmhammer/OSMa-Bench_dataset

OSMa-Bench Dataset

OSMa-Bench (Open Semantic Mapping Benchmark) dataset is a fully automatically generated dataset for evaluating the robustness of open semantic mapping and segmentation systems under varying indoor lighting conditions and robot movement dynamics. This dataset is part of OSMa-Bench pipeline.

• Homepage: OSMa-Bench Project Page

Dataset Summary

This dataset provides simulated RGB-D and semantically annotated posed sequences for evaluation of semantic mapping and segmentation, with a particular focus on handling dynamic lighting—a critical but often overlooked factor in existing benchmarks. It also includes a collection of automatically generated question–answer pairs across multiple categories to support the evaluation of scene-graph–based reasoning, offering a task-driven measure of how well a system’s reconstructed scene captures semantic relationships between objects.

The data is built upon two base datasets:

• ReplicaCAD: 22 scenes with 4 lighting configurations and a velocity modifier.
• Habitat Matterport 3D (HM3D): 8 scenes with 2 lighting configurations and a velocity modifier.

Installation

We offer two versions of the dataset: one with separate files and one as a single compressed archive. Use the following command to download the separated files (may be slow):

git xet install
git clone https://huggingface.co/datasets/warmhammer/OSMa-Bench_dataset -b main

and this command to download the compressed version (faster one):

git xet install
git clone https://huggingface.co/datasets/warmhammer/OSMa-Bench_dataset -b compressed
unzip data.zip

Data Configurations

The dataset includes the following configurations for the ReplicaCAD and HM3D scenes:

Configuration	Description
baseline	Static, non-uniformly distributed light sources (ReplicaCAD only)
dynamic_lighting	Lighting conditions change along the robot's path (ReplicaCAD only)
nominal_lights	The mesh itself emits light without added light sources
camera_light	An extra directed light source is attached to the camera
velocity	Sequences recorded at doubled nominal velocity

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Data Configurations

The dataset provides structured data for each scene, suitable for tasks like 3D scene understanding, visual question answering, and robotics. Each scene contains the following components:

Component	Description	Format / Example
RGB Images	Standard color images captured from different camera viewpoints.	frame000000.jpg, ...
Depth Images	Depth maps aligned with RGB images. Each pixel encodes depth in meters.	depth000000.png, ...
Semantic Masks	Pixel-wise semantic segmentation labels. Each pixel corresponds to a semantic class ID.	semantic000000.png, ...
Camera Trajectories	Flattened 4×4 transformation matrices representing camera poses for each frame.	traj.txt (one 4×4 matrix per line)
Question-Answer Pairs	Validated question-answer pairs related to the scene, optionally associated with specific frames.	validated_questions.json

VQA Question Categories

The dataset includes a automatically generated answer-question pairs with the following question types:

1. Binary General – Yes/No questions about the presence of objects and general scene characteristics
   Example: Is there a blue sofa?

2. Binary Existence-Based – Yes/No questions designed to track false positives by querying non-existent objects
   Example: Is there a piano?

3. Binary Logical – Yes/No questions with logical operators such as AND/OR
   Example: Is there a chair AND a table?

4. Measurement – Questions requiring numerical answers related to object counts or scene attributes
   Example: How many windows are present?

5. Object Attributes – Queries about object properties, including color, shape, and material
   Example: What color is the door?

6. Object Relations (Functional) – Questions about functional relationships between objects
   Example: Which object supports the table?

7. Object Relations (Spatial) – Queries about spatial placement of objects within the scene
   Example: What is in front of the staircase?

8. Comparison – Questions that compare object properties such as size, color, and position
   Example: Which is taller: the bookshelf or the lamp?

Citing OSMa-Bench Dataset

Using OSMa-Bench dataset in your research? Please cite following paper: OSMa-Bench arxiv.

@inproceedings{popov2025osmabench,
    title     = {OSMa-Bench: Evaluating Open Semantic Mapping Under Varying Lighting Conditions},
    author    = {Popov, Maxim and Kurkova, Regina and Iumanov, Mikhail and Mahmoud, Jaafar and Kolyubin, Sergey},
    booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year      = {2025}
}