Object-level point cloud extraction is relevant to robotic perception and may provide useful object-centric observations for mapping and SLAM. Many existing methods rely on per-frame instance segmentation, resulting in high computational cost and limited ability to extract multiple objects simultaneously in multi-object scenarios. This paper proposes an efficient MOT-assisted pipeline for multi-view object point cloud extraction. The pipeline first employs 2D multi-object tracking (MOT) to establish consistent object correspondences across views, and then combines monocular depth-based reconstruction with multi-view geometric association to estimate coarse object locations. An adaptive spherical proposal and a density-based refinement strategy are further introduced to extract clean object-specific point clouds while suppressing background noise and outliers. Experiments on the DTU, MVImgNet, and ScanNet++ datasets demonstrate the effectiveness of the proposed method. Relative to the unprocessed scene-level point cloud, the Chamfer Distance is reduced from $90.97$ mm to $12.52$ mm and Precision increases from $0.2998$ to $0.8776$ on DTU dataset Sequence 30. On MVImgNet, the Chamfer Distance decreases from $1.23$ m to $0.46$ m and Precision increases from $0.4777$ to $0.9337$, demonstrating effective removal of non-target scene points under real-world viewing conditions. Moreover, the proposed method reduces the cost of object-level association and provides competitive object-level extraction quality under the tested settings. Compared with the closely matched segmentation-based extraction, the proposed method provides lower measured object extraction runtime and built-in cross-view Track-ID association, while sacrificing some boundary accuracy.

In robotics, realizing 6D tracking, that is, tracking the three-dimensional position and orientation of moving objects in real-time, is a significant challenge. In this study, we extend fast pixel-wise posterior 3D (Fast-PWP3D), a method for estimating the position and posture simultaneously. The extended method involves the segmentation of the target region from multiple camera images using a 3D target model. The energy function was modified to handle multiviewpoint posture estimation. Consequently, the accuracy of target estimation and robustness against occlusion were improved.

In image sensing, measurements such as an object’s position or contour are typically obtained by analyzing digitized images. This method is widely used due to its simplicity. However, relative motion or inaccurate focus can cause motion and defocus blur, reducing measurement accuracy. Thus, video deblurring is essential. However, existing deep learning-based video deblurring methods struggle to balance high-quality deblurring, fast inference, and wide applicability. First, we propose a Current-Aware Temporal Fusion (CATF) framework, which focuses on the current frame in terms of both network architecture and modules. This reduces interference from unrelated features of neighboring frames and fully exploits current frame information, improving deblurring quality. Second, we introduce a Mixture-of-Experts module based on NAFBlocks (MoNAF), which adaptively selects expert structures according to the input features, reducing inference time. Third, we design a training strategy to support both sequential and temporally parallel inference. In sequential deblurring, we conduct experiments on the DVD, GoPro, and BSD datasets. Qualitative results show that our method effectively preserves image structures and fine details. Quantitative results further demonstrate that our method achieves clear advantages in terms of PSNR and SSIM. In particular, under the exposure setting of 3 ms–24 ms on the BSD dataset, our method achieves 33.09 dB PSNR and 0.9453 SSIM, indicating its effectiveness even in severely blurred scenarios. Meanwhile, our method achieves a good balance between deblurring quality and runtime efficiency. Moreover, the framework exhibits minimal error accumulation and performs effectively in temporal parallel computation. These results demonstrate that effective video deblurring serves as an important supporting technology for accurate image sensing.