NLOS-NeuS: Non-line-of-sight Neural Implicit Surface
This work addresses the challenge of inferring invisible scenes from indirect light for applications like imaging around corners, though it appears incremental as it builds on existing neural transient field methods.
The paper tackles the problem of reconstructing 3D surfaces in non-line-of-sight (NLOS) scenes by extending neural transient fields to neural implicit surfaces with a signed distance function (SDF), introducing constraints to learn correct SDFs, resulting in smooth surface reconstructions that preserve fine details compared to previous discretized methods.
Non-line-of-sight (NLOS) imaging is conducted to infer invisible scenes from indirect light on visible objects. The neural transient field (NeTF) was proposed for representing scenes as neural radiance fields in NLOS scenes. We propose NLOS neural implicit surface (NLOS-NeuS), which extends the NeTF to neural implicit surfaces with a signed distance function (SDF) for reconstructing three-dimensional surfaces in NLOS scenes. We introduce two constraints as loss functions for correctly learning an SDF to avoid non-zero level-set surfaces. We also introduce a lower bound constraint of an SDF based on the geometry of the first-returning photons. The experimental results indicate that these constraints are essential for learning a correct SDF in NLOS scenes. Compared with previous methods with discretized representation, NLOS-NeuS with the neural continuous representation enables us to reconstruct smooth surfaces while preserving fine details in NLOS scenes. To the best of our knowledge, this is the first study on neural implicit surfaces with volume rendering in NLOS scenes.