Non-line-of-sight (NLOS) imaging, recovering the hidden volume from indirect reflections, has attracted increasing attention due to its potential applications. Despite promising results, existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors, e.g., single fixed path compensation. Moreover, these approaches still possess limited generalization ability, particularly when dealing with scenes at a low signal-to-noise ratio (SNR). To overcome the above problems, we introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF). The LPC applies tailored path compensation coefficients to adapt to different objects in the scene, effectively reducing light wave attenuation, especially in distant regions. Meanwhile, the APF learns the precise Gaussian window of the illumination function for the phasor field, dynamically selecting the relevant spectrum band of the transient measurement. Experimental validations demonstrate that our proposed approach, only trained on synthetic data, exhibits the capability to seamlessly generalize across various real-world datasets captured by different imaging systems and characterized by low SNRs.

For a more compatible and consistent high dynamic range (HDR) viewing experience, a new image format with a double-layer structure has been developed recently, which incorporates an auxiliary Gain Map (GM) within a standard dynamic range (SDR) image for adaptive HDR display. This new format motivates us to introduce a new task termed Gain Map-based Inverse Tone Mapping (GM-ITM), which focuses on learning the corresponding GM of an SDR image instead of directly estimating its HDR counterpart, thereby enabling a more effective up-conversion by leveraging the advantages of GM. The main challenge in this task, however, is to accurately estimate regional intensity variation with the fluctuating peak value. To this end, we propose a dual-branch network named GMNet, consisting of a Local Contrast Restoration (LCR) branch and a Global Luminance Estimation (GLE) branch to capture pixel-wise and image-wise information for GM estimation. Moreover, to facilitate the future research of the GM-ITM task, we build both synthetic and real-world datasets for comprehensive evaluations: synthetic SDR-GM pairs are generated from existing HDR resources, and real-world SDR-GM pairs are captured by mobile devices. Extensive experiments on these datasets demonstrate the superiority of our proposed GMNet over existing HDR-related methods both quantitatively and qualitatively. The codes and datasets are available at https://github.com/qtlark/GMNet.

For magnetic inversion, recovering the location and boundaries of causative bodies is challenging. In this study, we propose an improved magnetic inversion method based on the L1 norm of the model and its gradients total variation (TV1). Four weights for the L1 norm and TV1 penalties are introduced in the L1-TV1 inversion, and their effects are discussed comprehensively. These weights are crucial for the inversion, controlling the shape, boundaries, and resolution of the resultant model. Based on extensive synthetic model tests, appropriate parameters are given to achieve good inversion performance. Numerical experiments demonstrate that the proposed approach can reproduce a consistent model with the real solution. Finally, we apply this method to the inversion of the actual data in the Yeshan region, east Jiangsu Province, China, and successfully image the underground magnetic structure.

Non-line-of-sight (NLOS) imaging aims to reconstruct scenes hidden from direct view and has broad applications in robotic vision, rescue operations, autonomous driving, and remote sensing. However, most existing methods rely on densely sampled transients from large, continuous relay surfaces, which limits their practicality in real-world scenarios with aperture constraints. To address this limitation, we propose an unsupervised zero-shot framework tailored for confocal NLOS imaging with aperture-limited relay surfaces. Our method leverages latent diffusion models to recover fully-sampled transients from undersampled versions by enforcing measurement consistency during the sampling process. To further improve recovered transient quality, we introduce a progressive recovery strategy that incrementally recovers missing transient values, effectively mitigating the impact of severe aperture limitations. In addition, to suppress error propagation during recovery, we develop a backpropagation-based error correction reconstruction algorithm that refines intermediate recovered transients by enforcing sparsity regularization in the voxel domain, enabling high-fidelity final reconstructions. Extensive experiments on both simulated and real-world datasets validate the robustness and generalization capability of our method across diverse aperture-limited relay surfaces. Notably, our method follows a zero-shot paradigm, requiring only a single pretraining stage without paired data or pattern-specific retraining, which makes it a more practical and generalizable framework for NLOS imaging.

Understanding scene dynamics relies heavily on optical flow and scene flow. Most existing flow estimation methods use low-rate RGB images and point clouds, and match the frames geometrically. However, this approach faces challenges in real-world scenes with intricate motion, occlusion, and noise. To tackle this problem, we combine point clouds with events, which introduce dynamic inter-frame information. We propose a bi-stream neural network that jointly estimates optical flow and scene flow. The event branch extracts dynamic information and estimates optical flow, while the point branch captures scene structure and estimate scene flow. A Spatio-temporal Fusion Block is introduced to fuse the complementary information from points and events. Additionally, we adopt a result-level fusion strategy for direct refinement between the flow predictions of the two branches. We evaluate our model on the real-world datasets DSEC and MVSEC. The experimental results demonstrate superior performance compared to existing methods.

Dynamic reconstruction in confocal non-line-of-sight imaging encounters great challenges since the dense raster-scanning manner limits the practical frame rate. A fewer pioneer works reconstruct high-resolution volumes from the under-scanning transient measurements but overlook temporal consistency among transient frames. To fully exploit multi-frame information, we propose the first spatial-temporal Mamba (ST-Mamba) based method tailored for dynamic reconstruction of transient videos. Our method capitalizes on neighbouring transient frames to aggregate the target 3D hidden volume. Specifically, the interleaved features extracted from the input transient frames are fed to the proposed ST-Mamba blocks, which leverage the time-resolving causality in transient measurement. The cross ST-Mamba blocks are then devised to integrate the adjacent transient features. The target high-resolution transient frame is subsequently recovered by the transient spreading module. After transient fusion and recovery, a physical-based network is employed to reconstruct the hidden volume. To tackle the substantial noise inherent in transient videos, we propose a wave-based loss function to impose constraints within the phasor field. Besides, we introduce a new dataset, comprising synthetic videos for training and real-world videos for evaluation. Extensive experiments showcase the superior performance of our method on both synthetic data and real world data captured by different imaging setups. The code and data are available at https://github.com/Depth2World/Dynamic_NLOS.