PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering

Recovering high quality surfaces from noisy point clouds, known as point cloud denoising, is a fundamental yet challenging problem in geometry processing. Most of the existing methods either directly denoise the noisy input or filter raw normals followed by updating point positions. Motivated by the essential interplay between point cloud denoising and normal filtering, we revisit point cloud denoising from a multitask perspective, and propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering. In particular, we introduce an auxiliary normal filtering task to help the overall network remove noise more effectively while preserving geometric features more accurately. In addition to the overall architecture, our network has two novel modules. On one hand, to improve noise removal performance, we design a shape-aware selector to construct the latent tangent space representation of the specific point by comprehensively considering the learned point and normal features and geometry priors. On the other hand, point features are more suitable for describing geometric details, and normal features are more conducive for representing geometric structures (e.g., sharp edges and corners). Combining point and normal features allows us to overcome their weaknesses. Thus, we design a feature refinement module to fuse point and normal features for better recovering geometric information. Extensive evaluations, comparisons, and ablation studies demonstrate that the proposed method outperforms state-of-the-arts for both point cloud denoising and normal filtering

Boosting Out-of-distribution Detection with Typical Features

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspective of typicality and regard the feature's high-probability region of the deep model as the feature's typical set. We propose to rectify the feature into its typical set and calculate the OOD score with the typical features to achieve reliable uncertainty estimation. The feature rectification can be conducted as a {plug-and-play} module with various OOD scores. We evaluate the superiority of our method on both the commonly used benchmark (CIFAR) and the more challenging high-resolution benchmark with large label space (ImageNet). Notably, our approach outperforms state-of-the-art methods by up to 5.11$\%$ in the average FPR95 on the ImageNet benchmark

The SNP rs961253 in 20p12.3 Is Associated with Colorectal Cancer Risk: A Case-Control Study and a Meta-Analysis of the Published Literature

Background: Colorectal cancer (CRC) is the third common cancer and the fourth leading cause of cancer death worldwide. A single nucleotide polymorphism (SNP), rs961253 located in 20p12, was firstly described to be associated with the increased risk of CRC in a genome-wide association study; however, more recent replication studies yielded controversial results. Methodology/Principal Findings: A hospital-based case-control study in a Chinese population was firstly performed, and then a meta-analysis combining the current and previously published studies were conducted to explore the real effect of rs961253 in CRC susceptibility. In the Chinese population including 641 cases and 1037 controls, per-A-allele conferred an OR of 1.60 (95 % CI = 1.26–2.02) under additive model. In the meta-analysis including 29859 cases and 29696 controls, per-Aallele have an OR of 1.13 (95 % CI = 1.09–1.18) under a random-effects model due to heterogeneity (P = 0.019). Nevertheless, the heterogeneity can be totally explained by ethnicity, with the tau 2 reduced to 0 after including ethnicity in metaregression model. In stratified analysis by ethnicity, per-A-allele had ORs of 1.34 (95 % CI = 1.20–1.50) and 1.11 (95% CI = 1.08–1.14) for Asian and European, respectively, without heterogeneity. Modest influence of each study was observed on overall estimate in sensitive analysis, and evident tendency to significant association was seen in cumulative analysis over time, together indicating the robust stability of the current results