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Updated: May 1, 2026

Photorealistic Learned Landscapes for Augmented Reality
Published on: June 27, 2025
Snapshot 3D Gaussian Splatting for Miniature Scenes
Abstract:
We present a snapshot imaging technique for recovering 3D surrounding views of miniature scenes. Due to their intricacy, miniature scenes with objects sized in millimeters are difficult to reconstruct. Yet miniatures are common in life and their 3D digitalization is desirable. We design a catadioptric imaging system with a single camera and multiple pairs of planar mirrors for snapshot 3D reconstruction of miniature scenes from a dollhouse perspective. We first present an in-depth analysis on how to configure catadioptric imaging systems that use a pair of planar mirrors. We derive mirror parameters (e.g., orientation and position) by solving a viewpoint mapping problem, which aims to determine viable mirror configuration that is able to provide reflection image from a desired viewpoint. By applying the design principle, We show a full-surround snapshot catadioptric imaging system built with eight pairs of planar mirrors. Specifically, we place the mirror pairs on nested pyramid surfaces with different angles for capturing surrounding multi-view images in a single shot. This would allow 3D reconstruction of dynamic scenes. Our mirror design is customizable based on the size of the scene for optimized view coverage. We use the 3D Gaussian Splatting (3DGS) representation for scene reconstruction and novel view synthesis. We overcome the challenge posed by our sparse view input by integrating visual hull-derived depth constraint. We perform experiments on rendered synthetic images and real images of a variety of miniature scenes that are captured by our custom-built imaging system. Experimental results demonstrate that our method outperforms state-of-the-art sparse-view and calibration-free 3DGS methods. We also show novel view synthesis results on dynamic miniature scenes (e.g., a live, moving insect).
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