Non-Line-of-Sight Surface Reconstruction Using the Directional Light-Cone Transform
Sean I. Young, David B. Lindell, Bernd Girod, David Taubman, Gordon Wetzstein
Abstract
We propose a joint albedo-normal approach to non-lineof-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT). While current NLOS imaging methods reconstruct either the albedo or surface normals of the hidden scene, the two quantities provide complementary information of the scene, so an efficient method to estimate both simultaneously is desirable. We formulate the recovery of the two quantities as a vector deconvolution problem, and solve it using the Cholesky-Wiener decomposition. We show that surfaces fitted non-parametrically using our recovered normals are more accurate than those produced with NLOS surface reconstruction methods recently proposed, and are 1,000× faster to compute than using inverse rendering.
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Install the CLIlune papers fulltext 80f1831f-07e0-4f4c-8633-77f350308a99Cited by top-tier papers10
- Deep Non-line-of-sight Imaging from Under-scanning MeasurementsYue Li, Yueyi Zhang, Juntian Ye, Feihu Xu et al.NeurIPS 2023 · 32 citations
- NLOS-NeuS: Non-line-of-sight Neural Implicit SurfaceYuki Fujimura, Takahiro Kushida, Takuya Funatomi, Yasuhiro MukaigawaICCV 2023 · 21 citations
- Enhancing Non-line-of-sight Imaging via Learnable Inverse Kernel and Attention MechanismsYanhua Yu, Siyuan Shen, Zi Wang, Binbin Huang et al.ICCV 2023 · 19 citations
- Virtual light transport matrices for non-line-of-sight imagingJulio Marco, Adrián Jarabo, Ji Hyun Nam, Xiaochun Liu et al.ICCV 2021 · 18 citations
- Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled TransientsXingyu Cui, Huanjing Yue, Song Li, Xiangjun Yin et al.NeurIPS 2024 · 13 citations
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