From Reality to Virtual Worlds: the Role of Photogrammetry in Game Development
arXiv · Graphics(2025)
Abstract
Photogrammetry is transforming digital content creation by enabling the rapid conversion of real-world objects into highly detailed 3D models. This paper evaluates the role of RealityCapture, a GPU-accelerated photogrammetry tool, in game development of Virtual Reality (VR). We assess its efficiency, reconstruction accuracy, and integration with Unreal Engine, comparing its advantages and limitations against traditional modeling workflows. Additionally, we examined user preferences between designed 3D assets and photogrammetry-generated models. The results revealed that while photogrammetry enhances realism and interactivity, users slightly preferred manually designed models for small, manipulable elements because of the level of detail. However, from a developer perspective, RealityCapture significantly reduces development time while maintaining geometric precision and photorealistic textures. Despite its reliance on high-performance hardware, its automation, scalability, and seamless integration with real-time rendering engines make it a valuable tool for game developers and VR creators. Future improvements in AI-driven optimization and cloud-based processing could enhance accessibility, broadening its applications in gaming, cultural heritage preservation, and simulation.
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest