Neuralangelo: High-Fidelity Neural Surface Reconstruction

Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.Comment: CVPR 2023, project page: https://research.nvidia.com/labs/dir/neuralangel

Can We Transfer Noise Patterns? An Multi-environment Spectrum Analysis Model Using Generated Cases

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system's learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://github.com/Magnomic/CNST

Distinguishable short-term effects of tea and water drinking on human saliva redox

Abstract Food consumption can alter the biochemistry and redox status of human saliva, and the serving temperature of food may also play a role. The study aimed to explore the immediate (3 min) and delayed (30 min) effects of hot tea (57 ± 0.5 °C) ingestion and cold tea (8 ± 0.5 °C) ingestion on the salivary flow rate and salivary redox-relevant attributes. The saliva was collected from 20 healthy adults before, 3-min after and 30-min after the tea ingestion. The hot or cold deionised water at the same temperatures were used as control. The salivary flow rate and redox markers in hot tea (HBT), cold tea (CBT), hot water (HW) and cold water (CW) group were analysed and compared. The results demonstrated that neither the black tea nor the water altered the salivary flow rate; the black tea immediately increased the salivary thiol (SH) and malondialdehyde (MDA) content while reduced salivary uric acid (UA) significantly. The tea ingestion showed a tendency to elevate the ferric reducing antioxidant power (FRAP) in saliva, although not significantly. The water ingestion decreased the MDA content immediately and increased the UA level significantly. Cold water was found to induce a greater delayed increase in total salivary total protein (TPC) than the hot water. In conclusion, the black tea ingestion affects the redox attributes of human saliva acutely and significantly, while the temperature of drink makes the secondary contribution