Abstract In this paper, we propose a deterministic approach for finding the first zero-crossing point of a differentiable, possibly multiextremal univariate function, that combines a Branch-and-Bound framework with piece-wise linear under- and overestimators derived from Lipschitz intervals. Two algorithms are presented: a baseline algorithm and a version enhanced with interval reduction techniques. Theoretical guarantees of correctness and finite termination of the introduced methods are established. Extensive numerical experiments on 27 benchmark problems demonstrate that the proposed methods outperform the existing interval Branch-and-Bound approach both in terms of running time and accuracy.

3D registration methods based on point-level information struggle in situations with noise, density variation, large-scale points, and small overlaps, while existing primitive-based methods are usually sensitive to tiny errors in the primitive extraction process. In this paper, we present a reliable and efficient global registration algorithm exploiting the RANdom SAmple Consensus (RANSAC) in the plane space instead of the point space. To improve the inlier ratio in the putative correspondences, we design an inner plane-based descriptor, termed Convex Hull Descriptor (CHD), and an inter plane-based descriptor, termed PLane Feature Histograms (PLFH), which take full advantage of plane contour shape and plane-wise relationship, respectively. Based on those new descriptors, we randomly select corresponding plane pairs to compute candidate transformations, followed by a hypotheses verification step to identify the optimal registration. Extensive tests on large-scale point sets demonstrate the effectiveness of our method, and that it notably improves registration performance compared to state-of-the-art methods in terms of efficiency and accuracy.

Abstract Neural Radiance Field (NeRF) can render complex 3D scenes with viewpoint‐dependent effects. However, less work has been devoted to exploring its limitations in high‐resolution environments, especially when upscaled to ultra‐high resolution (e.g., 4k). Specifically, existing NeRF‐based methods face severe limitations in reconstructing high‐resolution real scenes, for example, a large number of parameters, misalignment of the input data, and over‐smoothing of details. In this paper, we present a novel and effective framework, called De‐NeRF, based on NeRF and deformable convolutional network, to achieve high‐fidelity view synthesis in ultra‐high resolution scenes: (1) marrying the deformable convolution unit which can solve the problem of misaligned input of the high‐resolution data. (2) Presenting a density sparse voxel‐based approach which can greatly reduce the training time while rendering results with higher accuracy. Compared to existing high‐resolution NeRF methods, our approach improves the rendering quality of high‐frequency details and achieves better visual effects in 4K high‐resolution scenes.

Although the application of supervised deep learning in medical image analysis is still very successful, it mainly depends on the quantity and quality of labeled data; and it is time-consuming and labor-intensive to obtain 3D medical image annotation. Recently, contrastive learning has shown its remarkable ability for self-supervised learning and has achieved impressive results on many downstream tasks. In this study, we extend the popular contrastive learning medical image segmentation framework to 3D and design extra reconstruction loss for volumetric medical images to improve the performance of global contrastive learning. We evaluate our method on two public 3D medical image datasets of different modalities. Our proposed method achieves competitive results compared to other methods for different proportions of labeled data.

Specular highlight detection and removal is a fundamental problem in computer vision and image processing. In this paper, we present an efficient end-to-end deep learning model for automatically detecting and removing specular highlights in a single image. In particular, an encoder—decoder network is utilized to detect specular highlights, and then a novel Unet-Transformer network performs highlight removal; we append transformer modules instead of feature maps in the Unet architecture. We also introduce a highlight detection module as a mask to guide the removal task. Thus, these two networks can be jointly trained in an effective manner. Thanks to the hierarchical and global properties of the transformer mechanism, our framework is able to establish relationships between continuous self-attention layers, making it possible to directly model the mapping between the diffuse area and the specular highlight area, and reduce indeterminacy within areas containing strong specular highlight reflection. Experiments on public benchmark and real-world images demonstrate that our approach outperforms state-of-the-art methods for both highlight detection and removal tasks.

Specular reflections pose great challenges on various multimedia and computer vision tasks,e.g., image segmentation, detection and matching. In this paper, we build a large-scale Paired Specular-Diffuse (PSD) image dataset, where the images are carefully captured by using real-world objects and the ground-truth specular-free diffuse images are provided. To the best of our knowledge, this is the first real-world benchmark dataset for specular highlight removal task, which is useful for evaluating and encouraging new deep learning-based approaches. Given this dataset, we present a novel Generative Adversarial Network (GAN) for specular highlight removal from a single image by introducing the detection of specular reflection information as a guidance. Our network also makes full use of the attention mechanism and is able to directly model the mapping relation between the diffuse area and the specular highlight area without any explicit estimation of the illumination. Experimental results demonstrate that the proposed network is more effective to remove specular reflection components with the guidance of specular highlight detection than recent state-of-the-art methods.