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Large-Scale Indoor Visual-Geometric Multimodal Dataset and Benchmark for Novel View Synthesis
Junming Cao1,2, Xiting Zhao3, Sören Schwertfeger3
1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Sensors (Basel, Switzerland)
|September 14, 2024
Summary
We introduce a large-scale indoor dataset and benchmark to improve 3D scene reconstruction for augmented reality, virtual reality, and robotics. This resource addresses limitations in existing datasets, enabling more robust novel view synthesis (NVS) techniques.
Area of Science:
- Computer Vision
- 3D Reconstruction
- Robotics
Background:
- Accurate indoor environment reconstruction is vital for AR, VR, and robotics.
- Existing datasets lack scale, ground truth point clouds, and sufficient viewpoints for robust novel view synthesis (NVS).
Purpose of the Study:
- Introduce a large-scale indoor dataset and benchmark for evaluating NVS algorithms.
- Address limitations in current datasets to advance indoor scene reconstruction.
Main Methods:
- Collected panoramic image sequences, high-resolution point clouds, meshes, and textures for diverse indoor scenes.
- Developed a novel benchmark tailored for complex indoor environments.
Main Results:
- The dataset features challenging scenes like basements and long corridors.
- Provides comprehensive ground truth data for rigorous evaluation of NVS techniques.
Conclusions:
- The new dataset and benchmark will facilitate the development of more effective NVS solutions.
- Aims to advance the field of indoor scene reconstruction for real-world applications.
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