SDFReg: Learning Signed Distance Functions for Point Cloud Registration

Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy, partial, and density-varying point clouds. To this end, we propose a novel point cloud registration framework for these imperfect point clouds. By introducing a neural implicit representation, we replace the problem of rigid registration between point clouds with a registration problem between the point cloud and the neural implicit function. We then propose to alternately optimize the implicit function and the registration between the implicit function and point cloud. In this way, point cloud registration can be performed in a coarse-to-fine manner. By fully capitalizing on the capabilities of the neural implicit function without computing point correspondences, our method showcases remarkable robustness in the face of challenges such as noise, incompleteness, and density changes of point clouds

Improved Neural Radiance Fields Using Pseudo-depth and Fusion

Since the advent of Neural Radiance Fields, novel view synthesis has received tremendous attention. The existing approach for the generalization of radiance field reconstruction primarily constructs an encoding volume from nearby source images as additional inputs. However, these approaches cannot efficiently encode the geometric information of real scenes with various scale objects/structures. In this work, we propose constructing multi-scale encoding volumes and providing multi-scale geometry information to NeRF models. To make the constructed volumes as close as possible to the surfaces of objects in the scene and the rendered depth more accurate, we propose to perform depth prediction and radiance field reconstruction simultaneously. The predicted depth map will be used to supervise the rendered depth, narrow the depth range, and guide points sampling. Finally, the geometric information contained in point volume features may be inaccurate due to occlusion, lighting, etc. To this end, we propose enhancing the point volume feature from depth-guided neighbor feature fusion. Experiments demonstrate the superior performance of our method in both novel view synthesis and dense geometry modeling without per-scene optimization

Combustion reactivity of ilmenite with coal volatiles under steam gasification atmosphere

学位記番号：理工博甲6

Discovery of DNA Viruses in Wild-Caught Mosquitoes Using Small RNA High throughput Sequencing

BACKGROUND: Mosquito-borne infectious diseases pose a severe threat to public health in many areas of the world. Current methods for pathogen detection and surveillance are usually dependent on prior knowledge of the etiologic agents involved. Hence, efficient approaches are required for screening wild mosquito populations to detect known and unknown pathogens. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we explored the use of Next Generation Sequencing to identify viral agents in wild-caught mosquitoes. We extracted total RNA from different mosquito species from South China. Small 18-30 bp length RNA molecules were purified, reverse-transcribed into cDNA and sequenced using Illumina GAIIx instrumentation. Bioinformatic analyses to identify putative viral agents were conducted and the results confirmed by PCR. We identified a non-enveloped single-stranded DNA densovirus in the wild-caught Culex pipiens molestus mosquitoes. The majority of the viral transcripts (.>80% of the region) were covered by the small viral RNAs, with a few peaks of very high coverage obtained. The +/- strand sequence ratio of the small RNAs was approximately 7∶1, indicating that the molecules were mainly derived from the viral RNA transcripts. The small viral RNAs overlapped, enabling contig assembly of the viral genome sequence. We identified some small RNAs in the reverse repeat regions of the viral 5'- and 3' -untranslated regions where no transcripts were expected. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate for the first time that high throughput sequencing of small RNA is feasible for identifying viral agents in wild-caught mosquitoes. Our results show that it is possible to detect DNA viruses by sequencing the small RNAs obtained from insects, although the underlying mechanism of small viral RNA biogenesis is unclear. Our data and those of other researchers show that high throughput small RNA sequencing can be used for pathogen surveillance in wild mosquito vectors

The Mellin central projection transform

The central projection transform can be employed to extract invariant features by combining contour-based and region-based methods. However, the central projection transform only considers the accumulation of the pixels along the radial direction. Consequently, information along the radial direction is inevitably lost. In this paper, we propose the Mellin central projection transform to extract affine invariant features. The radial factor introduced by the Mellin transform, makes up for the loss of information along the radial direction by the central projection transform. The Mellin central projection transform can convert any object into a closed curve as a central projection transform, so the central projection transform is only a special case of the Mellin central projection transform. We prove that closed curves extracted from the original image and the affine transformed image by the Mellin central projection transform satisfy the same affine transform relationship. A method is provided for the extraction of affine invariants by employing the area of closed curves derived by the Mellin central projection transform. Experiments have been conducted on some printed Chinese characters and the results establish the invariance and robustness of the extracted features. doi:10.1017/S144618111600034

Enhancing fluxes through the mevalonate pathway in Saccharomyces cerevisiae by engineering the HMGR and β‐alanine metabolism

Summary Mevalonate (MVA) pathway is the core for terpene and sterol biosynthesis, whose metabolic flux influences the synthesis efficiency of such compounds. Saccharomyces cerevisiae is an attractive chassis for the native active MVA pathway. Here, the truncated form of Enterococcus faecalis MvaE with only 3‐Hydroxy‐3‐methylglutaryl coenzyme A reductase (HMGR) activity was found to be the most effective enzyme for MVA pathway flux using squalene as the metabolic marker, resulting in 431‐fold and 9‐fold increases of squalene content in haploid and industrial yeast strains respectively. Furthermore, a positive correlation between MVA metabolic flux and β‐alanine metabolic activity was found based on a metabolomic analysis. An industrial strain SQ3‐4 with high MVA metabolic flux was constructed by combined engineering HMGR activity, NADPH regeneration, cytosolic acetyl‐CoA supply and β‐alanine metabolism. The strain was further evaluated as the chassis for terpenoids production. Strain SQ3‐4‐CPS generated from expressing β‐caryophyllene synthase in SQ3‐4 produced 11.86 ± 0.09 mg l−1 β‐caryophyllene, while strain SQ3‐5 resulted from down‐regulation of ERG1 in SQ3‐4 produced 408.88 ± 0.09 mg l−1 squalene in shake flask cultivations. Strain SQ3‐5 produced 4.94 g l−1 squalene in fed‐batch fermentation in cane molasses medium, indicating the promising potential for cost‐effective production of squalene