Digital twins, detailed 3D replicas of real-world environments, are becoming essential across industries:
- 🏗️ Construction teams use them for planning, monitoring progress, and clash detection.
- 🏙️ Urban planners rely on them to simulate infrastructure changes and improve city design.
- 🏠 Real estate professionals showcase properties through immersive, interactive experiences.
- 🏭 Manufacturing and robotics use them for simulation, inspection, and training in virtual environments.
These applications demand accurate, photorealistic reconstructions, not just point clouds or rough meshes, but dynamic, view-dependent representations of the real world. As the demand for spatial understanding grows, so does the need for accessible, high-quality 3D capture pipelines.
However, capturing high-quality training data for these methods remains a significant challenge:
- Data Quality Issues: Real-world image and video data often suffer from problems like insufficient scene coverage, poor lighting, reflections, shadows, and occlusions, which negatively affect training outcomes.
- Lack of User Guidance: Novice users often lack best-practice knowledge of capture angles, camera settings, and lighting conditions, resulting in suboptimal datasets.
- Scale and Coverage: Collecting comprehensive data across large environments often requires multiple video captures. Ensuring complete coverage without gaps or redundancies is both time-consuming and challenging.
- Dynamic Elements: The presence of moving objects, such as people or vehicles, introduces additional complexity during both capture and reconstruction.
Our goal is to lower the barrier to creating digital twins by enabling users to capture scenes easily with a single 360° camera, without demanding expert workflows or controlled environments. The system is designed to support real-world capture conditions while working seamlessly across diverse camera inputs. By staying robust to these challenges and remaining forgiving to casual users, our pipeline brings reliable Gaussian Splatting reconstruction closer to everyday capture scenarios.