SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Scene Segmentation

We introduce SAMPro3D for zero-shot 3D indoor scene segmentation. Given the 3D point cloud and multiple posed 2D frames of 3D scenes, our approach segments 3D scenes by applying the pretrained Segment Anything Model (SAM) to 2D frames. Our key idea involves locating 3D points in scenes as natural 3D prompts to align their projected pixel prompts across frames, ensuring frame-consistency in both pixel prompts and their SAM-predicted masks. Moreover, we suggest filtering out low-quality 3D prompts based on feedback from all 2D frames, for enhancing segmentation quality. We also propose to consolidate different 3D prompts if they are segmenting the same object, bringing a more comprehensive segmentation. Notably, our method does not require any additional training on domain-specific data, enabling us to preserve the zero-shot power of SAM. Extensive qualitative and quantitative results show that our method consistently achieves higher quality and more diverse segmentation than previous zero-shot or fully supervised approaches, and in many cases even surpasses human-level annotations. The project page can be accessed at https://mutianxu.github.io/sampro3d/.Comment: Project page: https://mutianxu.github.io/sampro3d

Effect of vaginal microbiota on pregnancy outcomes of women from Northern China who conceived after IVF

ObjectiveThis study aimed to investigate the correlation between vaginal microbiota and pregnancy outcomes of women who achieved pregnancy via in vitro fertilization (IVF) in Northern China, and to determine a biomarker for evaluation of the risk of preterm births in these women.MethodsIn total, 19 women from Northern China women who conceived after IVF and 6 women who conceived naturally were recruited in this study. The vaginal samples of the healthy participants were collected throughout pregnancy, that is, during the first, second, and third trimesters. The V3–V4 region of 16S rRNA was used to analyze the vaginal microbiome, and the bioinformatic analysis was performed using QIIME Alpha and Beta diversity analysis.ResultsEither IVF group or Natural conception group, bacterial community diversities and total species number of vagnal samples from who delivered at term were significantly higher than those who delivered before term. Low abundance of vaginal bacteria indicates an increased risk of preterm delivery. Further, more abundant vaginal bacteria was found in first trimesters instead of the next two trimesters. Vignal samples collected during first trimester showed richer differences and more predictive value for pregnancy outcoes. In addition, the diversity of the vaginal bacterial community decreased as the gestational age increased, in all samples. Alloscardovia was only found in participants who conceived after IVF, and the percentage of Alloscardovia in viginal samples of normal delivery group is much higher than the samples from preterm delivery group.Vobrio specifically colonized in vagina of pregnant woman in AFT group (those who conceived after IVF (A), first trimester (F), and delivered at term (T)) and Sporosarcina was detected only in women with AFT and AST (those who conceived after IVF (A), second trimester (S), and delivered at term (T)). These data indicates that Alloscardovia, Vobrio and Sporosarcina have great potential in predicting pregnancy outcomes who pregnanted by vitro fertilizationConclusionsVaginal microbiota were more stable in women who conceived naturally and those who carried pregnancy to term. Oceanobacillus might act as a positive biomarker, whereas Sulfurospirillum and Propionispira may act as negative biomarkers for the risk of preterm birth

MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency

Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains an open problem due to the data sparsity and scene complexity. The conventional random masking paradigm used in 2D images often causes a high risk of ambiguity when recovering the masked region of 3D scenes. To this end, we propose a novel informative-preserved reconstruction, which explores local statistics to discover and preserve the representative structured points, effectively enhancing the pretext masking task for 3D scene understanding. Integrated with a progressive reconstruction manner, our method can concentrate on modeling regional geometry and enjoy less ambiguity for masked reconstruction. Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas. By elegantly combining informative-preserved reconstruction on masked areas and consistency self-distillation from unmasked areas, a unified framework called MM-3DScene is yielded. We conduct comprehensive experiments on a host of downstream tasks. The consistent improvement (e.g., +6.1 [email protected] on object detection and +2.2% mIoU on semantic segmentation) demonstrates the superiority of our approach

A randomized trial in comparison between planned cesarean and vaginal delivery on twin pregnancy

Objective: We explored the planned cesarean to vaginal delivery at the risk of fetal or neonatal death or serious neonatalmorbidity in women with twin pregnancies.Material and methods: Three hundred and forty-three pregnant women were divided into planned cesarean delivery(PCD) and vaginal delivery (PVD) groups (208 vs 135). In the planned-cesarean-delivery group, the rate of cesarean deliverywas 98.82%. Meanwhile, the rate of vaginal delivery was 51.27% in PVD group.Results: Women in the PCD group delivered earlier than that in the PVD group. However, the composite primary outcomeof the PCD group was like that of the PVD group. Certainly, the odds ratio of planned cesarean delivery and confidenceinterval of the PCD group was also like those of the PVD group.Conclusions: The risk of fetal or neonatal death or serious neonatal morbidity of planned-vaginal-delivery was like thoseof planned-vaginal-delivery in pregnant women with twin pregnancies

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods

Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models