Semantically Derived Geometric Constraints for {MVS} Reconstruction of Textureless Areas

Conventional multi-view stereo (MVS) approaches based on photo-consistency measures are generally robust, yet often fail in calculating valid depth pixel estimates in low textured areas of the scene. In this study, a novel approach is proposed to tackle this challenge by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and support depth and normal map estimation. Semantic class labels on image pixels are used to impose class-specific geometric constraints during multiview stereo, optimising the depth estimation on weakly supported, textureless areas, commonly present in urban scenarios of building facades, indoor scenes, or aerial datasets. Detecting dominant shapes, e.g., planes, with RANSAC, an adjusted cost function is introduced that combines and weighs both photometric and semantic scores propagating, thus, more accurate depth estimates. Being adaptive, it fills in apparent information gaps and smoothing local roughness in problematic regions while at the same time preserves important details. Experiments on benchmark and custom datasets demonstrate the effectiveness of the presented approach

Efficient error control in 3D mesh coding

Our recently proposed wavelet-based L-infinite-constrained coding approach for meshes ensures that the maximum error between the vertex positions in the original and decoded meshes is guaranteed to be lower than a given upper bound. Instantiations of both L-2 and L-infinite coding approaches are demonstrated for MESHGRID, which is a scalable 3D object encoding system, part of MPEG-4 AFX. In this survey paper, we compare the novel L-infinite distortion estimator against the L-2 distortion estimator which is typically employed in 3D mesh coding systems. In addition, we show that, under certain conditions, the L-infinite estimator can be exploited to approximate the Hausdorff distance in real-time implementation

Multiple View Stereo with quadtree-guided priors

Multi-View Stereo (MVS) algorithms rely on common photometric consistency measures and, therefore, in cases of low-textured surfaces tend to generate unreliable depth estimates or lack completeness due to matching ambiguities. Such textureless areas often imply dominant planar structures, typically occurring in man-made scenes. To support depth estimation in scenarios where challenging surfaces are present, we propose an extended PatchMatch pipeline using an adaptive accumulated matching cost calculation based on estimated prior plane hypotheses and the local textureness. Plane priors are detected in the object space and guided by quadtree structures in order to generate depth and normal hypothesis for every pixel, supporting, in this way, the propagation of more reliable depth estimates across the image. Experiments on the ETH3D high-resolution dataset and on custom real-world scenes demonstrate that our approach can favor the reconstruction of problematic regions by adding small complexity while preserving fine details in rich textured regions, achieving thus competitive results compared to state-of-the-art methods. The source code of the developed method is available at https://github.com/3DOM-FBK/openMVS

Research on the biology of Oenothera biennis L. species

Oenothera biennis L., a biennial plant is recommended in the treatment and healing of wounds, sore throat; it also possesses cleansing, emollient and antidiarrhoeia properties. Seeds of Oenothra biennis L. are rich in fatty oils possessing important therapeutically qualities due to the presence of linolenic gamma acid. The biological investigations carried out at the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca point out that vegetation period in plants in their first year is of about 182 days, and about 119 in plants in their 2nd vegetation