SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields

3D reconstruction from 2D image was extensively studied, training with depth supervision. To relax the dependence to costly-acquired datasets, we propose SceneRF, a self-supervised monocular scene reconstruction method using only posed image sequences for training. Fueled by the recent progress in neural radiance fields (NeRF) we optimize a radiance field though with explicit depth optimization and a novel probabilistic sampling strategy to efficiently handle large scenes. At inference, a single input image suffices to hallucinate novel depth views which are fused together to obtain 3D scene reconstruction. Thorough experiments demonstrate that we outperform all recent baselines for novel depth views synthesis and scene reconstruction, on indoor BundleFusion and outdoor SemanticKITTI. Our code is available at https://astra-vision.github.io/SceneRF.Comment: Project page: https://astra-vision.github.io/SceneR

COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation

Annotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several order of magnitudes. We propose COARSE3D, a novel architecture-agnostic contrastive learning strategy for 3D segmentation. Since contrastive learning requires rich and diverse examples as keys and anchors, we leverage a prototype memory bank capturing class-wise global dataset information efficiently into a small number of prototypes acting as keys. An entropy-driven sampling technique then allows us to select good pixels from predictions as anchors. Experiments on three projection-based backbones show we outperform baselines on three challenging real-world outdoor datasets, working with as low as 0.001% annotations

AN EVIDENCE FOR THE CONTRIBUTION OF ANAMMOX PROCESS IN NITROGEN REMOVAL FROM GROUNDWATER

Joint Research on Environmental Science and Technology for the Eart

COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation

International audienceAnnotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several order of magnitudes. We propose COARSE3D, a novel architecture-agnostic contrastive learning strategy for 3D segmentation. Since contrastive learning requires rich and diverse examples as keys and anchors, we leverage a prototype memory bank capturing class-wise global dataset information efficiently into a small number of prototypes acting as keys. An entropy-driven sampling technique then allows us to select good pixels from predictions as anchors. Experiments on three projection-based backbones show we outperform baselines on three challenging real-world outdoor datasets, working with as low as 0.001% annotations

PILOT SCALE STUDY ON AMMONIUM REMOVAL IN PHAP VAN WATER PLANT, HANOI CITY

Joint Research on Environmental Science and Technology for the Eart

Study of Temperature Effect on Luminous Flux of High Power Chip on Board Light Emitting Diode

High power chip on board light emitting diode (HPCOBLED) are a promising solid state light technology for a variety of lighting applications. In this study, we studied temperature effect on luminous flux of HPCOBLED using VMI-PR-001 system of Vietnam Metrology Institute. The results according to the temperature Tc is increasing, luminous flux reduced. Especially HPCOBLED is larger power, decreasing luminous flux is larger. Reason of this is chance power.   HPCOBLED model describes the temperature affection on luminous flux of HPCOBLED. The results of HPCOBLED model matched with that measured by the luminous flux measurement system (VMI-PR-001, Vietnam)

The Impact of Viral Marketing on Emotion and Impulse Buying Behavior: A Case Study of Online Fashion

Impulsive online shopping is becoming a habit for many young consumers, especially for fashion products. This study aims to analyze the influence of viral marketing on emotions and impulsive online shopping behavior of young people for fashion products in Vietnam. The results showed that viral marketing with characteristics such as entertainment, source credibility, visual appeal, informativeness, and irritation all had a significant impact on emotions and impulsive online shopping behavior. Therefore,some suggestions are proposed for applying viral marketing to promote impulsive online shopping behavior for fashion products. Keywords: Viral marketing, Impulse buying behavior, Online shopping, Emotions, Fashion. DOI: 10.7176/EJBM/15-7-03 Publication date: April 30th 202

Prevalence and risk factors for human papillomavirus infection among female sex workers in Hanoi and Ho Chi Minh City, Viet Nam: a cross-sectional study

OBJECTIVE: Female sex workers (FSWs) are at high risk of human papillomavirus (HPV) infections and cervical cancer due to their high number of sexual partners. The objectives of this study were to determine the prevalence of HPV and identify risk factors for high-risk HPV infection among FSWs in Hanoi and Ho Chi Minh City (HCMC), Viet Nam. METHODS: A cross-sectional study was conducted in Hanoi and HCMC between December 2017 and May 2018. We surveyed and screened 699 FSWs aged 318 years for HPV infection and abnormal cytology. A multivariable modified Cox regression model was used to determine risk factors for high-risk HPV infection. RESULTS: The overall prevalence of any HPV, high-risk HPV and HPV-16/18 infection in the 699 FSWs was 26.3%, 17.6% and 4.0%, respectively, and were similar in both cities. Multiple infections were identified in 127 participants (69.0%). HPV-52 was the most prevalent (7%), followed by HPV-58 (6%). Abnormal cytology was detected in 91 participants (13.0%). FSWs who are divorced (adjusted prevalence ratio [aPR]: 1.96, 95% confidence interval [CI]: 1.01-3.81), widowed (aPR: 3.26, 95% CI: 1.49-7.12) or living alone (aPR: 1.85, 95% CI: 1.01-3.39) were associated with a higher prevalence of high-risk HPV infection. DISCUSSION: Almost one in five FSWs in Viet Nam are infected with high-risk HPV. This highlights the importance of prevention strategies such as HPV vaccination and screening in this high-risk group

Anal human papillomavirus prevalence and risk factors among men who have sex with men in Vietnam.

OBJECTIVES: Men who have sex with men (MSM) are at risk of human papillomavirus (HPV)-related cancers, while published data are scarce. This study determined HPV prevalence and risk factors in MSM in Vietnam to inform HPV prevention strategies in this key population. METHODS: A cross-sectional study of 799 MSM aged 16-50 years was conducted in Vietnam in 2017-2018. Information was collected on risk behaviours, and knowledge of HPV and anal cancer; rectal swabs were taken to detect anal HPV infection. An in-house polymerase chain reaction and Genoflow HPV array test kit were used for HPV detection and genotyping. RESULTS: The median age of the study participants was 25 years (range 18-52). Overall prevalence of any HPV and HPV16/18 infection was 32.3% and 11.0%, respectively. A higher prevalence of high-risk HPV infection to all 14 types tested was found in Ho Chi Minh City (30.9%) than in Hanoi (18.4%). High-risk HPV infection was associated with inconsistent condom use and history of engaging in sex under the influence of drugs (adjusted odds ratio (aOR), 2.27; 95% CI, 1.48-10.67), as well as having multiple sexual partners (aOR, 1.01; 95% CI, 1.00-1.02). CONCLUSIONS: High-risk anal HPV infections in Vietnamese MSM were significantly associated with risky sexual behaviours. A targeted HPV vaccination strategy would have substantial benefit for MSM in Vietnam

MonoScene: Monocular 3D Semantic Scene Completion

Accepted at CVPR 2022. Project page: https://cv-rits.github.io/MonoScene/International audienceMonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its semantics. Our framework relies on successive 2D and 3D UNets bridged by a novel 2D-3D features projection inspiring from optics and introduces a 3D context relation prior to enforce spatio-semantic consistency. Along with architectural contributions, we introduce novel global scene and local frustums losses. Experiments show we outperform the literature on all metrics and datasets while hallucinating plausible scenery even beyond the camera field of view. Our code and trained models are available at https://github.com/cv-rits/MonoScen