Capturing the full plenoptic light transport across spatial, angular, and temporal dimensions has long been a pursuit in computational imaging, yet it remains fundamentally constrained by the high dimensionality of the sampling space and the physical inaccessibility of scene regions due to self-occlusions. While time-resolved imaging records the temporal axis, existing methods are bottlenecked by the combinatorial complexity of the plenoptic function. This high dimensionality makes dense omni-dimensional sampling physically prohibitive. Simultaneously, tight coupling between illumination and viewpoint in current systems also precludes the full acquisition of plenoptic light transport. In this work, we present GenPIE, a Generative Plenoptic Imager designed to bridge the gap between sparse physical observations and high-dimensional light transport. We introduce a decoupled laser-detector hardware setup that enables independent control over illumination and detection, allowing for active probing of indirect light paths. To overcome the ill-posedness of sparse sampling and physical blind spots, we propose a generative inverse transient rendering framework. Our approach leverages 3D foundation models to provide strong semantic and 3D geometric priors for initialization, which are subsequently refined through a differentiable transient path tracer to ensure physically grounded adherence to the Transient Rendering Equation. We demonstrate that GenPIE supports a range of applications that are challenging for steady-state or purely neural methods, including disentangling multi-bounce light transport directly from captured transient videos, time unwarping, and time-resolved relighting. The project page is at https://wangzh1.github.io/GenPIE.

In radiometric measurements, the emissivity of natural objects exhibits a dependence on the viewing angle. Ignoring the angular effect of surface emissivity can increase the uncertainty of land surface temperature (LST) retrievals. To mitigate this issue, we evaluated the simulation performance of 11 parametric kernel-driven models (KDMs) and developed directional emissivity models using MYD21 and MYD03 products. Afterward, the directional and classification-based emissivities were input into the refined GSW algorithm to retrieve LSTs with and without considering angular effects (LST_GSW_DE and LST_GSW_CE, respectively). Coupled with the MYD21 LST product (LST_TES), three LSTs were evaluated via SURFRAD in situ data and ERA5-Land products. The main findings were as follows: (1) The RMSEs of directional emissivity simulated by different KDMs ranged from ˜0.0003 to ˜0.001, and their performance differences were generally slight, indicating that parameterized KDMs demonstrate reliable simulation performance in satellite-based directional emissivity modeling. (2) The directional emissivity simulation performances of different KDMs were ranked as follows: dual-kernel model (with both hotspot and base shape kernels) ≥ multikernel model > single-kernel model. The USEA and GUTA-sparse models exhibited advantages over the other KDMs when simulating impervious surfaces during the daytime. (3) We evaluated the three types of retrieved LSTs via SURFRAD in situ data. The rankings of the RMSE and MBE values were consistent: LST_TES was optimal, followed by LST_GSW_DE and LST_GSW_CE, with average RMSEs of 2.47 K, 2.62 K, and 2.80 K, respectively. Furthermore, we evaluated the three types of retrieved LSTs against the ERA5-Land data, and the rankings of the RMSE and MBE values were also consistent: LST_TES was comparable to (slightly better than) LST_GSW_DE in some seasons and consistently better than LST_GSW_CE. The average RMSEs were 2.45 K, 2.52 K, and 2.60 K. In addition, the RMSE and MBE values at different VZAs for the three LSTs increased with increasing VZA, especially when the VZA was greater than 40°. The results demonstrated that it is feasible to use KDMs to simulate directional emissivity from satellite data, offering theoretical interpretability and addressing the issues of discrete and missing emissivity data. Future studies could be devoted to establishing new KDMs or kernels that conform to different land surface and solar illumination conditions to improve the LST retrieval accuracy.