Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis. Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions. From a probabilistic perspective, these methods effectively model the relationships between latent factors and the low-rank tensor using Dirac delta distributions. However, tensor low-rank decomposition is inherently non-unique, leading to a multimodal distribution over possible solutions. Critically, such prior knowledge is rarely available in practical scenarios, particularly regarding the optimal rank structure and contraction rules. To address this issue, we propose a score-based model that eliminates the need for predefined structural or distributional assumptions, enabling the learning of compatibility between tensors and latent factors. Specifically, a neural network is designed to learn the energy function, which is optimized via score matching to capture the gradient of the joint log-probability of tensor entries and latent factors. Our method allows for modeling structures and distributions beyond the Dirac delta assumption. Moreover, integrating the block coordinate descent (BCD) algorithm with the proposed smooth regularization enables the model to perform both tensor completion and denoising. Experimental results demonstrate significant performance improvements across various tensor types, including sparse and continuous-time tensors, as well as visual data.

Monocular visual odometry (VO) is crucial for the application of various autonomous systems. However, the inherent scale ambiguity issue in monocular methods greatly limits their performance in pose estimation. In this paper, we propose a hybrid monocular VO system named KPDepth-VO, which solves camera pose from monocular video based on sparse keypoints. To estimate the scale-consistent relative pose, we present a novel photometric-sensitive depth uncertainty model that accounts for the depth uncertainty introduced by limitations in the photometric error constraint. We also introduce an uncertainty-aware scale recovery strategy that incorporates depth uncertainty for reliable scale alignment. Additionally, we propose a novel difference attention mechanism to construct a point filter that effectively filters out less distinctive points, ensuring high-quality matches for more accurate and efficient pose estimation in the proposed system. Experimental results on the KITTI dataset and Oxford Robotcar dataset demonstrate that our system can predict scale-consistent trajectories from monocular videos and achieve state-of-the-art performance among similar methods. Meanwhile, the depth network within our system achieves competitive depth estimation performance on KITTI depth benchmark.

Lane detection plays a critical role in the field of autonomous driving. Most previous lane detection methods focus exclusively on analyzing individual images and overlook the inter-frame dynamics, while car-mounted cameras capture continuous streams amenable to leveraging intra-video context. In challenging scenes with blur, occlusion, or illumination variations, considering visible lanes from previous frames can aid current frame interpretation. To tackle above challenges, we introduce a parallel heterogeneous framework, called PHNet, for video lane detection. First, a novel router automatically analyzes multi-level features to determine the inference route of candidates with different visual cues. Then, a cross-frame attention mechanism leverages relationships between current candidates and positive embeddings from past frame to aggregate contextual cues. Unlike existing offline video lane detection methods, which face limitations in handling long video sequences and real-time video streams due to computational constraints, our proposed method operates as an online framework capable of processing clips of any length. Our method demonstrates robust lane detection through temporally association modeling and efficient online inference. The extensive experiments on public benchmark show that PHNet has superior performance versus state-of-the-art video lane detection methods.