PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce
  Lidar
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.
6588f61c067630f69f681217
Tzofi Klinghoffer
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Xiaoyu Xiang
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Siddharth Somasundaram
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Yuchen Fan
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Christian Richardt
claimed_verified
2025-02-13T20:36:56.127Z
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/avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
Christian Richardt
user
cr333
6588f61c067630f69f68121c
Ramesh Raskar
6588f61c067630f69f68121d
Rakesh Ranjan
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce
  Lidar
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.
A single-photon lidar-based approach combines NeRF and two-bounce light measurements to achieve accurate single-view 3D reconstruction without data priors or controlled lighting.
neural radiance fields NeRF time-of-flight data single-photon avalanche diode two-bounce optical paths lidar transient data occluded geometry sensor spatial-resolution sensor temporal-resolution