Equivariant Light Field Convolution and Transformer

3D reconstruction and novel view rendering can greatly benefit from geometric priors when the input views are not sufficient in terms of coverage and inter-view baselines. Deep learning of geometric priors from 2D images often requires each image to be represented in a $2D$ canonical frame and the prior to be learned in a given or learned $3D$ canonical frame. In this paper, given only the relative poses of the cameras, we show how to learn priors from multiple views equivariant to coordinate frame transformations by proposing an $SE(3)$-equivariant convolution and transformer in the space of rays in 3D. This enables the creation of a light field that remains equivariant to the choice of coordinate frame. The light field as defined in our work, refers both to the radiance field and the feature field defined on the ray space. We model the ray space, the domain of the light field, as a homogeneous space of $SE(3)$ and introduce the $SE(3)$-equivariant convolution in ray space. Depending on the output domain of the convolution, we present convolution-based $SE(3)$-equivariant maps from ray space to ray space and to $\mathbb{R}^3$. Our mathematical framework allows us to go beyond convolution to $SE(3)$-equivariant attention in the ray space. We demonstrate how to tailor and adapt the equivariant convolution and transformer in the tasks of equivariant neural rendering and $3D$ reconstruction from multiple views. We demonstrate $SE(3)$-equivariance by obtaining robust results in roto-translated datasets without performing transformation augmentation.Comment: 46 page

The Analysis and Possible Solutions on the Problem of Female Undergraduate Students’ Employment Difficulties

The global financial crisis, which burst out in 2009, had caused great decline on global economy. The employment problems in every country are extreme severe. Chinese economy has grew in a relatively lower speed in recent years and the amount of undergraduates has been increasing all along. The employment issue for undergraduates in our country has been into a dilemma. Under such circumstance, the problem of female college students employment has been much more serious. This article will fully illustrate the status quo of the difficulties in employment for female college students and analyze on the causes and reasons of the employment difficulties for female college students as well as further come up with some possible solutions to release the situation of employment difficulties

EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision

We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple, effective, and robust geometric algorithm that can segment objects in 3D scenes without annotations or training on scenes. We achieve such unsupervised segmentation by exploiting single object shape priors. We make two novel steps in that direction. First, we introduce equivariant shape representations to this problem to eliminate the complexity induced by the variation in object configuration. Second, we propose a novel EM algorithm that can iteratively refine segmentation masks using the equivariant shape prior. We collect a novel real dataset Chairs and Mugs that contains various object configurations and novel scenes in order to verify the effectiveness and robustness of our method. Experimental results demonstrate that our method achieves consistent and robust performance across different scenes where the (weakly) supervised methods may fail. Code and data available at https://www.cis.upenn.edu/~leijh/projects/efemComment: Accepted by CVPR2023, project page https://www.cis.upenn.edu/~leijh/projects/efe

OSP: Boosting Distributed Model Training with 2-stage Synchronization

Distributed deep learning (DDL) is a promising research area, which aims to increase the efficiency of training deep learning tasks with large size of datasets and models. As the computation capability of DDL nodes continues to increase, the network connection between nodes is becoming a major bottleneck. Various methods of gradient compression and improved model synchronization have been proposed to address this bottleneck in Parameter-Server-based DDL. However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively. To address these challenges, we propose a new model synchronization method named Overlapped Synchronization Parallel (OSP), which achieves efficient communication with a 2-stage synchronization approach and uses Local-Gradient-based Parameter correction (LGP) to avoid accuracy loss caused by stale parameters. The prototype of OSP has been implemented using PyTorch and evaluated on commonly used deep learning models and datasets with a 9-node testbed. Evaluation results show that OSP can achieve up to 50\% improvement in throughput without accuracy loss compared to popular synchronization models.Comment: Copyright Owner/Author | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in ICPP 202

Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images

We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape reconstruction from multiple views uses discrete representations such as point clouds or voxels, while continuous surface generation approaches lack multi-view consistency. We address these issues by designing neural networks capable of generating high-quality parametric 3D surfaces which are also consistent between views. Furthermore, the generated 3D surfaces preserve accurate image pixel to 3D surface point correspondences, allowing us to lift texture information to reconstruct shapes with rich geometry and appearance. Our method is supervised and trained on a public dataset of shapes from common object categories. Quantitative results indicate that our method significantly outperforms previous work, while qualitative results demonstrate the high quality of our reconstructions.Comment: ECCV 202

Online Education in Human Parasitology during the COVID-19 Pandemic in Wuhan: Our Experiences, Challenges, and Perspectives

Traditional face-to-face teaching in medical schools has been suspended during the global COVID-19 pandemic, and remote online learning has consequently been implemented as an emergency measure. This study aims to share our experiences in exploring online teaching of human parasitology and to discuss the possible advantages, challenges and perspectives that we observed during Wuhan’s lockdown due to the pandemic. The application of online education is likely to be an indispensable component of post-COVID-19 interactive online parasitology courses. Our experience might provide an example for the future development of interactive online medical courses