Semantic Scene Completion (SSC) aims to reconstruct complete 3D scenes with precise voxel-wise semantics from the single-view incomplete input data, a crucial but highly challenging problem for scene understanding. Although SSC has seen significant progress due to the introduction of 2D semantic priors in recent years, the occluded parts, especially the rear-view of the scenes, are still poorly completed and segmented. To ameliorate this issue, we propose a novel deep learning framework for 3D SSC, named Planar Convolution and Attention-based Network (PCANet), to effectively extend high-precision predictions of the front-view surface to the rear-view occluded areas. Specifically, we decompose the traditional convolutional layer into three successive planar convolutions to form a Planar Convolution Residual (PCR) block, which maintains the planar features of the 3D scene. Afterward, the Planar Attention Module (PAM) is proposed to capture three different planar attentions and harvest the global context from the front surface to the rear occluded areas to improve the overall accuracy. Extensive experiments on the real NYU and NYUCAD datasets and the synthetic SUNCG-RGBD dataset demonstrate that our proposed framework can generate high-quality SSC results in both front and rear views and outperforms the state-of-the-art approaches trained in an end-to-end manner without additional data.

In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing the quality of images shot over various geographical areas, particularly for high-resolution images. However, there are some shortcomings of the widely used Convolutional Neural Networks (CNNs) and Transformers-based CD methods. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Additionally, the commonly used information-fusion methods for pre- and post-change images often lead to information loss or redundancy, resulting in inaccurate edge detection. To address these issues, we propose an Iterative Mamba Diffusion Change Detection (IMDCD) approach to iteratively integrate various pieces of information and efficiently produce fine-grained CD maps. Specifically, the Swin-Mamba-Encoder (SME) within Mamba-CD (MCD) is employed as a semantic feature extractor, capable of modeling long-range relationships with linear computability. Moreover, we introduce the Variable State Space CD (VSS-CD) module, which extracts abundant CD features by training the matrix parameters within the designed State Space Change Detection (SS-CD). The computed high-dimensional CD feature is integrated into the noise predictor using a novel Global Hybrid Attention Transformer (GHAT) while low-dimensional CD features are utilized to calibrate prior CD results at each iterative step, progressively refining the generated outcomes. IMDCD exhibits a high performance across multiple datasets such as the CDD, WHU, LEVIR, and OSCD, marking a significant advancement in the methodologies within the CD field of RS. The code for this work is available on GitHub.

The non-local (NL) network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most of the current popular NL models tend to ignore the phenomenon that the calculated attention map appears to be very noisy, containing interclass and intraclass inconsistencies, which lowers the accuracy and reliability of the NL methods. In this article, we figuratively denote these inconsistencies as attention noises and explore the solutions to denoise them. Specifically, we inventively propose a denoised NL network, which consists of two primary modules, i.e., the global rectifying (GR) block and the local retention (LR) block, to eliminate the interclass and intraclass noises, respectively. First, GR adopts the class-level predictions to capture a binary map to distinguish whether the selected two pixels belong to the same category. Second, LR captures the ignored local dependencies and further uses them to rectify the unwanted hollows in the attention map. The experimental results on two challenging semantic segmentation datasets demonstrate the superior performance of our model. Without any external training data, our proposed denoised NL can achieve the state-of-the-art performance of 83.5% and 46.69% mean of classwise intersection over union (mIoU) on Cityscapes and ADE20K, respectively.

Multi-UAV collaborative mapping in communication-constrained indoor environments is often hampered by a trade-off between overall map refinement and the timely completion of safety-relevant shared regions. In high-density or unmapped areas, network congestion can delay the updates that matter most for close-proximity coordination, because standard bandwidth allocation does not distinguish between general map refinement and hotspot-related spatial data. To address this issue, we propose a resilient scheduling framework that prioritizes globally useful map updates while improving safety-relevant hotspot completeness under unreliable links. At its core is a Safety Reserve allocation strategy for “hotspot” submaps—areas where UAV trajectories overlap or approach unknown frontiers. By enforcing this reserve, the system directs a limited uplink budget to hotspot-related updates earlier during congestion. To remain useful under packet loss, we implement a prefix-decodable hierarchical data structure over a lightweight stateless protocol, allowing immediate fusion of valid partial updates. The framework identifies hotspots using feedback from a Lambda-Field risk model and a truncated least squares solver with graduated non-convexity (TLS–GNC) pose-graph optimizer. Experiments on S3DIS and ScanNet under partition-based two-agent emulation show that the proposed method improves hotspot-band completeness and progressive mapping quality over the tested baselines, especially under packet loss.

The construction of vectorized High-Definition (HD) maps from onboard surround-view cameras has become a significant focus in autonomous driving. However, current map vector estimation pipelines face two key limitations: input-agnostic queries struggle to capture complex map structures, and the view transformation leads to information loss. These issues often result in inaccurate shape restoration or missing instances in map predictions. To address this concern, we propose a novel approach, namely PerCMap, which explicitly exploits clues from perspective-view features at both instance and point level. Specifically, at instance level, we propose Cross-view Instance Activation (CIA) to activate instance queries across surround-view images, thereby helping the model recover the instance attributes of map vectors. At point level, we design Dual-view Point Embedding (DPE), which fuses features from both views to generate input-aware positional embeddings and improve the accuracy of point coordinate estimation. Extensive experiments on nuScenes and Argoverse 2 demonstrate that PerCMap achieves strong and consistent performance across benchmarks, reaching 67.1 and 70.5 mAP, respectively.