Accurate odometry is crucial for mapping and localization of autonomous vehicles in unknown environment. Deep neural networks have shown significant promise in self-supervised odometry, enabling pose estimation from consecutive sensor inputs. However, existing self-supervised visual odometry methods face scale ambiguity issue due to the inaccuracy of relative depth estimation, and self-supervised LiDAR odometry methods suffer from sparse data and the difficulty of correspondence searching. To address these challenges, we propose Self-FO, a self-supervised multi-sensor fusion odometry framework that effectively integrates image and point cloud data and overcomes the limitations of single-sensor methods. First, we utilize the inferred depth map as the fusion medium for image and point cloud, and realize multi-layer visual-LiDAR feature extraction and fusion through a novel homogeneous and heterogeneous channel exchange strategies. Then, we introduce a feature alignment and pose refinement module to fine-tune the coarse pose. By leveraging 2D-3D correspondences and a cross-modal attention mechanism, this module guides the image and point cloud features to focus on consistent scene-level observations and aligns features adaptively, significantly improving the accuracy of correspondence searching and enhancing the robustness and consistency of multimodal feature representations. Extensive experiments on KITTI and KITTI-360 dataset demonstrate that Self-FO outperforms existing learning-based odometry methods, delivering superior performance and scalability.

Segmentation of micro-adhesive spots in high-power laser packaging is challenged by morphological variability, complex backgrounds, and blurred edges, causing traditional models to fail from “feature dilution.” Inspired by physical optics knowledge, we propose a scattering neural representation framework guided by Rayleigh scattering theory. We first pretrain a denoising diffusion model, using light scattering properties, including wavelength, scattering angle, and particle number density as an inductive bias to generate high signal-to-noise ratio target features while suppressing background clutter. Subsequently, three synergistic attention modules, including an adaptive dual-attention module, an edge attention module, and a small object enhancement module, refine target features by dynamically expanding the receptive field, sharpening boundaries, and enhancing microtarget responses. Extensive experiments on proprietary and public datasets demonstrate that our model significantly outperforms state-of-the-art methods in precise segmentation and background interference suppression. This work translates physical insights into architectural advantages, establishing an efficient and interpretable paradigm for addressing the persistent challenge of industrial small object segmentation.

Liver tumor segmentation plays an essential role in diagnosis and treatment of hepatocellular carcinoma or metastasis. However, accurate and automatic tumor segmentation remains a challenging task, owing to vague boundaries and large variations in shapes, sizes, and locations of liver tumors. In this paper, we propose a novel hybrid end-to-end network, called TD-Net, which incorporates Transformer and direction information into convolution network to segment liver tumor from CT images automatically. The proposed TD-Net is composed of a shared encoder, two decoding branches, four skip connections, and a direction guidance block. The shared encoder is utilized to extract multi-level feature information, and the two decoding branches are respectively designed to produce initial segmentation map and direction information. To preserve spatial information, four skip connections are used to concatenate each encoder layer and its corresponding decoder layer, and in the fourth skip connection a Transformer module is constructed to extract global context. Furthermore, a direction guidance block is well-designed to rectify feature maps to further improve segmentation accuracy. Extensive experiments conducted on public LiTS and 3DIRCADb datasets validate that the proposed TD-Net can effectively segment liver tumor from CT images in an end-to-end manner and its segmentation accuracy surpasses those of many existing methods.