Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this article, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be easily optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios.

We propose a real-time visual-inertial dense SLAM system that utilizes the online data streams from back-to-back dual fisheye cameras setup, providing 360◦coverage of the environment. Firstly, we employ a sliding-window-based front-end to estimate real-time poses from the binocular fisheye images and IMU data. Then, we implement a lightweight panoramic depth completion network based on multi-basis depth representation. The network takes panoramic images (obtained by stitching dual-fisheye images with extrinsics and intrinsic parameters) and sparse depths (generated by the front-end local tracking) as input and predicts multiple depth bases along with corresponding confidence as output. The final dense depth is the linear combination of the multiple depth bases. Thanks to the multi-basis depth representation, we can continuously optimize the 360° depth with the traditional optimizer to achieve higher global consistency in depth. We conducted experiments on both simulated and real-world datasets to evaluate our method. The results demonstrate that the proposed method outperforms SoTA methods in terms of depth prediction and 3D reconstruction. In addition, we develop a demo that can run on a mobile to demonstrate the real-time capabilities of our method.

Risk item identification is vital for protecting the health of ecommerce trades. Existing solutions prefer to model structure information besides item attributes and optimize parameters in cross-entropy (CE) manners. However, the few labeled and imbalanced supervision in real-world scenarios usually results in poor generalization of CE optimization. More seriously, the pattern-level difference of risk items is often neglected in binary supervised learning, leading to limited performance. In this paper, we propose a novel Clustering-based Supervised Contrastive Learning (CSCL) to address the two challenges. CSCL first devises a contrastive heterogeneous graph neural network that fully exploits multiple risk relations in contrastive learning, keeping generalization performance. It then designs a clustering-based reweighted sampling strategy to search informative positive and negative training instances for effective pattern-level optimization. We test the performance on Xianyu Platform, and experimental results demonstrate that CSCL outperforms all baselines.

Prohibited item detection is an important problem in e-commerce, where the goal is to detect illegal items online for evading risks and stemming crimes. Traditional solutions usually mine evidence from individual instances, while current efforts try employing advanced Graph Neural Networks (GNN) to utilize multiple risk-relevant structures of items. However, it still remains two essential challenges, including weak structure and weak supervision. This work proposes the Risk Graph Structure Learning model (RGSL) for prohibited item detection. RGSL first introduces structure learning into large-scale risk graphs, to reduce noisy connections and add similar pairs. It then designs the pairwise training mechanism, which transforms the detection process as a metric learning from candidates to their similar prohibited items. Furthermore, RGSL generates risk-aware item representations and searches risk-relevant pairs for structure learning iteratively. We test RGSL on three real-world scenarios, and the improvements to baselines are up to 21.91% in AP and 18.28% in MAX-F1. Meanwhile, RGSL has been deployed on an e-commerce platform, and the improvements to traditional solutions are up to 23.59% in [email protected] and 6.52% in [email protected]

Graph-level representation learning is to learn low-dimensional representation for the entire graph, which has shown a large impact on real-world applications. Recently, limited by expensive labeled data, contrastive learning based graph-level representation learning attracts considerable attention. However, these methods mainly focus on graph augmentation for positive samples, while the effect of negative samples is less explored. In this paper, we study the impact of negative samples on learning graph-level representations, and a novel curriculum contrastive learning framework for self-supervised graph-level representation, called CuCo, is proposed. Specifically, we introduce four graph augmentation techniques to obtain the positive and negative samples, and utilize graph neural networks to learn their representations. Then a scoring function is proposed to sort negative samples from easy to hard and a pacing function is to automatically select the negative samples in each training procedure. Extensive experiments on fifteen graph classification real-world datasets, as well as the parameter analysis, well demonstrate that our proposed CuCo yields truly encouraging results in terms of performance on classification and convergence.