We address the “long-range ambiguity” problem for unsupervised non-rigid point cloud correspondence, where corresponding points own inconsistent features while different local regions are spatially or geometrically similar. Previous methods struggle with this problem, since local reference frames (LRF) or coordinate-based methods struggle to exclude locally similar or spatially near mismatches, and widely used independent geometric relations might be inconsistent under non-rigid deformation, introducing extra ambiguity. To this end, we propose a novel robust context modeling module (RCM) to alleviatelong-range ambiguityin two aspects: 1) RCM tackles the ambiguity problem by introducing inter-relation attention (IRA), which mines robust cues from the interplay between relative geometric relations. 2) RCM enhances features with accessiblelong-rangeinformation from IRAs, following a local-to-global manner. Our method shows significant improvements in multiple benchmarks, with accurate correspondence over rotation and large deformation perturbation. Specifically, our method achieves a new state-of-the-art performance with correspondence accuracy of 33.9% and mean error of 4.2 on the SURREAL benchmark.

We present a Gaussian Splatting method for surface reconstruction using sparse input views. Previous methods relying on dense views struggle with extremely sparse Structure-from-Motion points for initialization. While learning-based Multi-view Stereo (MVS) provides dense 3D points, directly combining it with Gaussian Splatting leads to suboptimal results due to the ill-posed nature of sparse-view geometric optimization. We propose Sparse2DGS, an MVS-initialized Gaussian Splatting pipeline for complete and accurate reconstruction. Our key insight is to incorporate the geometric-prioritized enhancement schemes, allowing for direct and robust geometric learning under illposed conditions. Sparse2DGS outperforms existing methods by notable margins while being 2× faster than the NeRF-based fine-tuning approach. Code is available at https://github.com/Wuuu3511/Sparse2DGS.

Monocular depth estimation from RGB images plays a pivotal role in 3D vision. However, its accuracy can deteriorate in challenging environments such as nighttime or adverse weather conditions. While long-wave infrared cameras offer stable imaging in such challenging conditions, they are inherently low-resolution, lacking rich texture and semantics as delivered by the RGB image. Current methods focus solely on a single modality due to the difficulties to identify and integrate faithful depth cues from both sources. To address these issues, this paper presents a novel approach that identifies and integrates dominant cross-modality depth features with a learning-based framework. Concretely, we independently compute the coarse depth maps with separate networks by fully utilizing the individual depth cues from each modality. As the advantageous depth spreads across both modalities, we propose a novel confidence loss steering a confidence predictor network to yield a confidence map specifying latent potential depth areas. With the resulting confidence map, we propose a multi-modal fusion network that fuses the final depth in an end-to-end manner. Harnessing the proposed pipeline, our method demonstrates the ability of robust depth estimation in a variety of difficult scenarios. Experimental results on the challenging$\text{MS}^{2}$and ViViD++ datasets demonstrate the effectiveness and robustness of our method.

Metamorphic testing has become one mainstream technique to address the notorious oracle problem in software testing, thanks to its great successes in revealing real-life bugs in a wide variety of software systems. Metamorphic relations, the core component of metamorphic testing, have continuously attracted research interests from both academia and industry. In the last decade, a rapidly increasing number of studies have been conducted to systematically generate metamorphic relations from various sources and for different application domains. In this article, based on the systematic review on the state of the art for metamorphic relations’ generation, we summarize and highlight visions for further advancing the theory and techniques for identifying and constructing metamorphic relations and discuss promising research directions in related areas.

Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, di-rectly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related meth-ods either seek selective aggregation or improve aggregated depth in the 2D space, both are unable to handle geomet-ric inconsistency in the cost volume effectively. In this pa-per, we propose GoMVS to aggregate geometrically consis-tent costs, yielding better utilization of adjacent geometries. More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geomet-ric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals, then uses the correspondence to propa-gate adjacent costs to the reference geometry, followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU, Tanks & Temple, and ETH3D datasets. Notably, our method ranks 1st on the Tanks & Temple Advanced benchmark. Code is available at https://github.com/Wuuu3511IGoMVS.

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method.

Self-supervised monocular depth estimation has succeeded in learning scene geometry from only image pairs or sequences. However, it is still highly ill-posed for self-supervised depth estimation to generate high-quality depth maps with both global high accuracy and local fine details. To address this issue, we propose a novel frequency-based recurrent refinement scheme to improve the self-supervised depth estimation. Since the global and local depth representation can be correlated to high/low frequency coefficients in the frequency domain, we propose a frequency-based recurrent depth coefficient refinement (RDCR) scheme, which progressively refines both low frequency and high frequency depth coefficients with an RNN-based architecture in a multi-level manner. During the recurrent process, the depth coefficients generated from the previous time step are used as the input to generate the current depth coefficients, yielding progressively optimized depth estimations. Meanwhile, considering that the depth details often appear in areas with high image frequency, we further improve depth details during the RDCR process by leveraging the image-based high frequency components. Specifically, in each RDCR module, we enhance the high frequency depth representations by selecting and feeding the informative image-based high frequency features with a learned feature weighting mask. Extensive experiments show that the proposed method achieves globally accurate estimation with fine local details, outperforming other self-supervised methods in both quantitative and qualitative comparisons.

Summary The “last mile” problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real‐time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain‐based UAV delivery system to resolve the “last mile” problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC‐based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the “last mile” problem and address the security issues in UAV delivery systems.

Recent years have seen a rapid development of machine learning based multi-module unmanned aerial vehicle (UAV) systems. To address the oracle problem in autonomous systems, numerous studies have been conducted to use metamorphic testing to automatically generate test scenes for various modules, e.g., those in self-driving cars. However, as most of the studies are based on unit testing including end-to-end model-based testing, a similar testing approach may not be equally effective for UAV systems where multiple modules are working closely together. Therefore, in this paper, instead of unit testing, we propose a novel metamorphic system testing framework for UAV, named MSTU, to detect the defects in multi-module UAV systems. A preliminary evaluation plan to apply MSTU on an emerging autonomous multi-module UAV system is also presented to demonstrate the feasibility of the proposed testing framework.

Abstract Nowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and restricted battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV‐based smart systems and the quality of experience (QoE) of end users. Recently, Mobile Edge Computing (MEC) which provisions computing resources close to the mobile end devices has become a promising solution. However, since high‐speed UAV often flies through the signal range of the different edge nodes, the interruption of services in the MEC‐based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In this paper, we investigate the task migration issue for multiple UAVs in the MEC‐based UAV delivery system. Specifically, we propose an energy‐aware decision‐making strategy for the dynamic task migration named GAD to optimize the UAV energy consumption. Given the real‐time system status and QoS constraints, and through a dynamic two‐tier decision‐making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, viz. No Migration, Data Migration Only, Cold Migration, and Live Migration. Experimental results based on a real‐world scenario show that our strategy can well outperform other baseline strategies in various metrics including the flying distances and the energy consumption of UAVs.

As an ideal infrastructure for smart services, Fog Computing is becoming the next wave of IT investment harnessing the successful models of Cloud Computing and latest technologies such as 5G and Internet of Things (IoT). However, the development of Fog Computing systems is a big challenge due to its complex, heterogeneous and distributed nature. Currently, there are a few SDKs released by some public Cloud service providers to support the development of Fog services in a top-down fashion as the key motive is to leverage their business Cloud services. However, Fog Computing systems are usually designed in a bottom-up fashion as the major functionalities are centred around the Edge Nodes and the End Devices. Meanwhile, significant efforts are required to verify the conformance of software behaviours as the collaboration between the End Devices, Edge Nodes and Cloud Servers is vital to the success of a Fog Computing System. Therefore, a holistically designed software development platform is urgently required. In this paper, we propose TDD4Fog, a test-driven software development platform for Fog Computing systems. Following the Test-Driven Development (TDD) methodology and a bottom-up design fashion, TDD4Fog supports the microservice architecture and provides the Test-Driven utilities such as metamorphic testing, mutation testing and random testing for the whole software development lifecycle of Fog Computing systems. To demonstrate the feasibility of TDD4Fog, we have presented some preliminary results on the key components of TDD4Fog and discussed some important future research directions.

At present, most of the smart systems are based on cloud computing, and massive data generated at the smart end device will need to be transferred to the cloud where AI models are deployed. Therefore, a big challenge for smart system engineers is that cloud based smart systems often face issues such as network congestion and high latency. In recent years, mobile edge computing (MEC) is becoming a promising solution which supports computation-intensive tasks such as deep learning through computation offloading to the servers located at the local network edge. To take full advantage of MEC, an effective collaboration between the end device and the edge server is essential. In this paper, as an initial investigation, we propose Edge4Sys, a Device-Edge Collaborative Framework for MEC based Smart System. Specifically, we employ the deep learning based user identification process in a MEC-based UAV (Unmanned Aerial Vehicle) delivery system as a case study to demonstrate the effectiveness of the proposed framework which can significantly reduce the network traffic and the response time.

Self-supervised depth estimation has shown great prospects in inferring 3D structures using purely unannotated images. However, its performance usually drops when trained on the images with changing brightness and moving objects. In this paper, we address this issue by enhancing the robustness of the self-supervised paradigm using a set of image-based and geometry-based constraints. Our contributions are threefold, 1) we propose a gradient-based robust photometric loss which restrains the false supervisory signals caused by brightness changes, 2) we propose to filter out the unreliable areas that violate the rigid assumption by a novel combined selective mask, which is computed on the forward pass of the network by leveraging the inter-loss consistency and the loss-gradient consistency, and 3) we constrain the motion estimation network to generate across-frame consistent motions via proposing a triplet-based cycle consistency constraint. Extensive experiments conducted on KITTI, Cityscape and Make3D datasets demonstrate the superiority of our method, that the proposed method can effectively handle complex scenes with changing brightness and object motions. Both qualitative and quantitative results show that the proposed method outperforms the state-of-the-art methods.

In this paper, we propose a hybrid Structure-from-Motion scheme which combines the strength of both global and local incremental SfM methods to get a drift-free and accurate estimation with lower time consumption. More specifically, we propose to construct a robust maximum leaf spanning tree (RMLST) from the initial scene graph and further expand it to a robust graph (RG) to grasp the global picture of camera distribution and scene structure. Then the views in the robust graph are solved in global manner as an initial estimation. After that, the remaining views are estimated with the proposed community-based local incremental approach to guarantee local accuracy and scalability. Bundle adjustment is conducted to optimize the estimation. Experiments show that our method is robust and free from the scene drift as global SfM, and shows much better efficiency than incremental approaches. Besides, our algorithm achieves higher accuracy compared with the state-of-the-art methods.