Despite significant progress in point cloud upsampling, most existing methods rely heavily on supervised training with paired data, which are often difficult to acquire. Moreover, the inherent lack of explicit connectivity in point clouds makes it difficult to achieve both continuous and uniform densification while accurately recovering fine geometric structures. To address these problems, we propose an upsampling model that treats this task as saliency-aware implicit surface sampling, enabling self-supervised and fine-grained point densification. Central to our idea is correlating implicit surface reconstruction with salient point identification, and carrying out sampling on the saliency-aware surface representation. Motivated by this, we introduce a salient point detector along with a corresponding implicit surface-based interpolator, and a geometry filter, upon which we develop a three-stage architecture consisting of a pre-trained saliency guidance block, a saliency-aware enhancer, and an upsampler. The guidance block captures meaningful shape patterns to prevent detail loss and incomplete recovery, while the enhancer facilitates detail enhancement in complex salient regions. These components are integrated with the upsampler to generate dense results that accurately retain both global shape and meticulous structures. In comparison with state-of-the-art methods, our model significantly improves the upsampling quality. Extensive experiments conducted on various datasets, comprising both synthetic and real-world captured shapes, demonstrate the flexibility and availability of our method in processing the point clouds across different scales and distributions.

In robotic surgery, instrument presence labels are typically recorded alongside video streams, offering a cost-effective alternative to manual annotations for segmentation tasks. Label-supervised surgical instrument segmentation (SIS), a weakly supervised segmentation setting where only instrument presence labels are available, remains underexplored due to its inherently ill-posed nature. Temporal information plays a vital role in capturing sequential dependencies, thereby enhancing representation learning even under incomplete supervision. This paper extends a two-stage label-supervised segmentation framework by leveraging the temporal characteristics of surgical videos from three perspectives. First, a temporal equivariance constraint is introduced to enforce pixel-level consistency across adjacent frames. Second, a class-aware semantic continuity constraint is applied to preserve coherence between global and local regions over time. Third, temporally-enhanced pseudo masks are generated from consecutive frames to suppress irrelevant regions and improve segmentation accuracy. We evaluate our method on two surgical video datasets: the Cholec80 cholecystectomy benchmark and a real-world robotic left lateral segmentectomy (RLLS) dataset. Instance-level instrument annotations, sampled at regular intervals and validated by an experienced clinician, provide a reliable basis for evaluation. Experimental results demonstrate that our method consistently achieves favorable performances over state-of-the-art methods. These findings highlight the effectiveness of incorporating temporal constraints into label-supervised frameworks, offering a promising strategy to reduce annotation costs and advance surgical video analysis.

Despite the success of recent upsampling approaches, generating high-resolution point sets with uniform distribution and meticulous structures is still challenging. Unlike existing methods that only take spatial information of the raw data into account, we regard point cloud upsampling as generating dense point clouds from deformable topology. Motivated by this, we present SPU-PMD, a self-supervised topological mesh deformation network, for 3D densification. As a cascaded framework, our architecture is formu-lated by a series of coarse mesh interpolator and mesh de-formers. At each stage, the mesh interpolator first produces the initial dense point clouds via mesh interpolation, which allows the model to perceive the primitive topology better. Meanwhile, the deformer infers the morphing by estimating the movements of mesh nodes and reconstructs the de-scriptive topology structure. By associating mesh deformation with feature expansion, this module progressively re-fines point clouds' surface uniformity and structural details. To demonstrate the effectiveness of the proposed method, extensive quantitative and qualitative experiments are con-ducted on synthetic and real-scanned 3D data. Also, we compare it with state-of-the-art techniques to further illus-trate the superiority of our network. The project page is: https://github.com/lyz21/spU-PMd.

Spatial outlier detection in wireless sensor network (WSN) can detect the objects whose non-spatial attributes are significantly different from their spatial neighbors, so as to ensure the reliability and accuracy of sensor data before decision-making process. The main drawback of existing spatial outlier detection algorithms is high user-dependency, which is not suitable for dynamic WSN data. This paper proposes an adaptive spatial outlier detection algorithm with no parameter, which can calculate the number of spatial neighbors based on global stability, and also can find outlier detection threshold automatically. This paper also proposes a new measurement for the degree of outlierness: spatial local outlier value (SLOV), which reflects the significance of spatial outliers more intuitively. Experimental results on WSN data of Intel Berkeley laboratory show that the spatial neighborhood found by nearest neighbor algorithm based on global stability conforms to the actual spatial distribution, and the algorithm is proved to be viable. Also, from experimental results on WSN data of GreenOrbs, it can be observed that the proposed algorithm can determine the number of outliers automatically according to the data characteristics, and the detection accuracy is generally over 80%, higher than that of existing SLOM and SLOF algorithm. It can be concluded that the adaptive spatial outlier detection algorithm proposed in this paper can detect spatial outliers without any parameter specified and with comparatively high detection rate, so it is verified to be feasible for wireless sensor network.