PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar

Tzofi Klinghoffer

3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF), while popular for view synthesis and 3D reconstruction, are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions, which may not be physically accurate, or shadows observed by RGB cameras, which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF, using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar, we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition, we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices, such as phones, tablets, and headsets. 

PlatoNeRF is named after the allegory of Plato's Cave, in which reality is discerned from shadows cast on a cave wall.

We propose PlatoNeRF: a method to recover scene geometry from a single view using two-bounce signals captured by a single-photon lidar. (a) A laser illuminates a scene point, which diffusely reflects light in all directions. The reflected light illuminates the rest of the scene and casts shadows. Light that returns to the lidar sensor provides information about the visible scene, and cast shadows provide information about occluded portions of the scene. (b) The lidar sensor captures 3D time-of-flight images. (c) By aggregating several such images (by scanning the position of the laser), we are able to reconstruct the entire 3D scene geometry with volumetric rendering.

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Accolades: Oral and Best Paper Award Candidate at CVPR 2024