This study introduces a novel method to mitigate temporal illumination variations in whisk-broom hyperspectral imaging under varying environmental illumination conditions. Whisk-broom hyperspectral imaging captures high resolution spectra pixel-by-pixel sequentially, a process susceptible to sunlight fluctuations over time, particularly when imaging cultural artifacts outdoors. Despite sunlight's broad spectrum, its variability over time can compromise the quality of hyperspectral images, affecting data analysis. Prior strategies suggested a supplementary single compensating vertical scan alongside the standard row-wise raster scan. However, this strategy fails when the additional single vertical scan is performed near or on a black frame. Building on this, our study proposes using two scans, one performed orthogonally to the traditional row-wise scan, to counteract illumination variations. Furthermore, we formulate the compensation problem in logarithmic space, exploiting the low-dimensional structure of the reflectance and illumination spectra. Using total variation penalization in the cost function enhances smoothness. Our method achieves robust compensation for changes in environmental illumination. We also demonstrate that we can use multiple columns from the column-wise scan without significantly decreasing the compensation quality while reducing acquisition time. We illustrate the application of our methods to hyperspectral images of stained-glass windows of the historic Cathedrale Notre-Dame d'Amiens in France.

This paper introduces a novel method for detailed 3D shape reconstruction utilizing thermal polarization cues. Unlike state-of-the-art methods, the proposed approach is independent of illumination and material properties. In this paper, we formulate a general theory of polarization observation and show that long-wave infrared (LWIR) polarimetric imaging is free from the ambiguities that affect visible polarization analyses. Subsequently, we propose a method for recovering detailed 3D shapes using multi-view thermal polarimetric images. Experimental results demonstrate that our approach effectively reconstructs fine details in transparent, translucent, and heterogeneous objects, outperforming existing techniques.

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. Project page: https://yfujimura.github.io/nlos-neus/

In this paper, we propose a new cue of depth sensing using thermal radiation. Our method realizes passive, texture independent, far range, and dark scene applicability, which can broaden the depth sensing subjects. A key ob-servation is that thermal radiation is attenuated by the air and is wavelength dependent. By modeling the wavelength-dependent attenuation by the air and building a multi-spectral LWIR measurement system, we can jointly estimate the depth, temperature, and emissivity of the target. We analytically show the capability of the thermal radiation cue and show the effectiveness of the method in real-world scenes using an imaging system with a few bandpass filters.

Phase ambiguity is a major problem in the depth measurement in either time-of-flight or phase shifting. Resolving the ambiguity using a low frequency pattern sacrifices the depth precision, and using multiple frequencies requires a number of observations. In this paper, we propose a phase disambiguation method that combines temporal and spatial modulation so that the high depth precision is preserved while the number of observation is small. A key observation is that the phase ambiguities of temporal and spatial domains appear differently with respect to the depth. Using this difference, the phase can disambiguate for a wider range of interest. We develop a prototype to show the effectiveness of our method through real-world experiments.