RESUMO
Understanding and modeling perceived properties of sky-dome illumination is an important but challenging problem due to the interplay of several factors such as the materials and geometries of the objects present in the scene being observed. Existing models of sky-dome illumination focus on the physical properties of the sky. However, these parametric models often do not align well with the properties perceived by a human observer. In this work, drawing inspiration from the Hosek-Wilkie sky-dome model, we investigate the perceptual properties of outdoor illumination. For this purpose, we perform a large-scale user study via crowdsourcing to collect a dataset of perceived illumination properties (scattering, glare, and brightness) for different combinations of geometries and materials under a variety of outdoor illuminations, totaling 5,000 distinct images. We perform a thorough statistical analysis of the collected data which reveals several interesting effects. For instance, our analysis shows that when there are objects in the scene made of rough materials, the perceived scattering of the sky increases. Furthermore, we utilize our extensive collection of images and their corresponding perceptual attributes to train a predictor. This predictor, when provided with a single image as input, generates an estimation of perceived illumination properties that align with human perceptual judgments. Accurately estimating perceived illumination properties can greatly enhance the overall quality of integrating virtual objects into real scene photographs. Consequently, we showcase various applications of our predictor. For instance, we demonstrate its utility as a luminance editing tool for showcasing virtual objects in outdoor scenes.
RESUMO
Increased complexity of microbial networks can contribute to increased biodiversity and multifunctionality and thus crop productivity. However, it is not clear which combination ratio of regular and controlled-release urea will increase the soil microbial community complexity and improve maize yield in the North China Plain. To address this knowledge gap, a 2-year field experiment was conducted to explore the effects of the combination of regular (U) and controlled release (S) urea ratios [no fertilizer control (CT), regular urea alone (U), controlled-release urea alone (S), controlled-release urea mixed with regular urea 3:7 (SU3), controlled-release urea mixed with regular urea 5:5 (SU5), and controlled-release urea mixed with regular urea 7:3 (SU7)] on XianYu 688 yield and its rhizosphere and bulk soil microbial community composition and network complexity at different fertility stages. The combination of controlled-release and regular urea increased the N agronomic efficiency, N partial factors productivity, maize yield, and grain number per spike, with the maximum maize yield (9,186 kg ha-1) being achieved when the ratio of controlled-release urea to regular urea was 3:7 (SU3, p < 0.05). Maize yield increased by 13% in the SU3 treatment compared to the CT treatment. Rhizosphere soil microbial diversity remained stable at the silking stage of maize while increased at the physiological maturity stage of maize, with the increasing controlled-release to regular N fertilizer ratios (from 3:7 to 7:3, p < 0.05). This result suggests that a combination of regular and controlled-release N fertilizer can still substantially increase soil microbial diversity in the later stages of maize growth. The combination of controlled-release and regular urea is more effective in improving microbial network total links and average degree, and N agronomic efficiency (R 2 = 0.79, p < 0.01), N partial factor productivity (R 2 = 0.79, p < 0.01), spikes per unit area (R 2 = 0.54, p < 0.05), and maize yield (R 2 = 0.42, p < 0.05) increased with the microbial network complexity. This result indicates that the higher microbial network complexity is strongly associated with the higher N agronomic efficiency and N partial factors productivity and maize yield. In conclusion, the ratio of controlled-release to regular urea at SU3 not only increases the yield of maize and N agronomic efficiency but also enhances microbial diversity and network complexity in the North China Plain.
