World PulseNow
Newsletter

Accurate and Efficient Surface Reconstruction from Point Clouds via Geometry-Aware Local Adaptation

arXiv — cs.CV•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent submission titled 'Accurate and Efficient Surface Reconstruction from Point Clouds via Geometry-Aware Local Adaptation' on arXiv highlights a novel approach to point cloud surface reconstruction, which is crucial for fields such as infrastructure inspection. This method leverages advances in deep learning to improve reconstruction accuracy by adaptively modulating the size and spacing of local regions according to the curvature of the input point cloud. Previous techniques often employed uniform local regions of fixed size, which restricted their adaptability to the complexities of different geometries. By overcoming these limitations, the proposed method not only enhances the efficiency of reconstruction but also broadens the applicability of point cloud processing in real-world scenarios, making it a notable contribution to the field.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios
arXiv — cs.LG13 hours ago
Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios
PositiveArtificial Intelligence
Meta-SimGNN is a novel WiFi localization system that combines graph neural networks with meta-learning to enhance localization generalization and robustness. It addresses the limitations of existing deep learning-based localization methods, which primarily focus on environmental variations while neglecting the impact of device configuration changes. By introducing a fine-grained channel state information (CSI) graph construction scheme, Meta-SimGNN adapts to variations in the number of access points (APs) and improves usability in diverse scenarios.
Read full article
via arXiv — cs.LG
Algebraformer: A Neural Approach to Linear Systems
arXiv — cs.LG13 hours ago
Algebraformer: A Neural Approach to Linear Systems
PositiveArtificial Intelligence
The recent development of Algebraformer, a Transformer-based architecture, aims to address the challenges of solving ill-conditioned linear systems. Traditional numerical methods often require extensive parameter tuning and domain expertise to ensure accuracy. Algebraformer proposes an end-to-end learned model that efficiently represents matrix and vector inputs, achieving scalable inference with a memory complexity of O(n^2). This innovation could significantly enhance the reliability and stability of solutions in various application-driven linear problems.
Read full article
via arXiv — cs.LG
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
arXiv — cs.LG13 hours ago
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
PositiveArtificial Intelligence
Saliency maps are essential for providing visual explanations in deep learning, yet there remains a significant lack of consensus regarding their purpose and alignment with user queries. This uncertainty complicates the evaluation and practical application of these explanation methods. To address this, a new taxonomy called Reference-Frame × Granularity (RFxG) is proposed, categorizing saliency explanations based on two axes: Reference-Frame and Granularity. This framework highlights limitations in existing evaluation metrics, emphasizing the need for a more comprehensive approach.
Read full article
via arXiv — cs.LG
MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
arXiv — cs.CV13 hours ago
MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
PositiveArtificial Intelligence
MicroEvoEval is introduced as a systematic evaluation framework aimed at predicting image-based microstructure evolution. This framework addresses critical gaps in the current methodologies, particularly the lack of standardized benchmarks for deep learning models in microstructure simulation. The study evaluates 14 different models across four MicroEvo tasks, focusing on both numerical accuracy and physical fidelity, thereby enhancing the reliability of microstructure predictions in materials design.
Read full article
via arXiv — cs.CV
CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities
arXiv — cs.CV13 hours ago
CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities
PositiveArtificial Intelligence
The Cross-Modal Compositional Self-Distillation (CCSD) framework has been proposed to enhance brain tumor segmentation from multi-modal MRI scans. This method addresses the challenge of missing modalities in clinical settings, which can hinder the performance of deep learning models. By utilizing a shared-specific encoder-decoder architecture and two self-distillation strategies, CCSD aims to improve the robustness and accuracy of segmentation, ultimately aiding in clinical diagnosis and treatment planning.
Read full article
via arXiv — cs.CV
A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
arXiv — cs.CV13 hours ago
A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
PositiveArtificial Intelligence
Underwater image restoration and enhancement are essential for correcting color distortion and restoring details in images, which are crucial for various underwater visual tasks. Current deep learning methods face challenges due to the lack of high-quality paired datasets, as pristine reference labels are hard to obtain in underwater environments. This paper proposes a novel approach that utilizes in-air natural images as reference targets, translating them into underwater-degraded versions to create synthetic datasets that provide authentic supervision for model training.
Read full article
via arXiv — cs.CV
Doppler Invariant CNN for Signal Classification
arXiv — cs.LG13 hours ago
Doppler Invariant CNN for Signal Classification
PositiveArtificial Intelligence
The paper presents a Doppler Invariant Convolutional Neural Network (CNN) designed for automatic signal classification in radio spectrum monitoring. It addresses the limitations of existing deep learning models that rely on Doppler augmentation, which can hinder training efficiency and interpretability. The proposed architecture utilizes complex-valued layers and adaptive polyphase sampling to achieve frequency bin shift invariance, demonstrating consistent classification accuracy with and without random Doppler shifts using a synthetic dataset.
Read full article
via arXiv — cs.LG
Deep Learning for Short-Term Precipitation Prediction in Four Major Indian Cities: A ConvLSTM Approach with Explainable AI
arXiv — cs.LG3 days ago
Deep Learning for Short-Term Precipitation Prediction in Four Major Indian Cities: A ConvLSTM Approach with Explainable AI
PositiveArtificial Intelligence
A new study presents a deep learning framework for short-term precipitation prediction in Bengaluru, Mumbai, Delhi, and Kolkata, utilizing a hybrid CNN-ConvLSTM architecture. This model, trained on multi-decadal ERA5 reanalysis data, aims to enhance transparency in weather forecasting. The models achieved varying root mean square error (RMSE) values: 0.21 mm/day for Bengaluru, 0.52 mm/day for Mumbai, 0.48 mm/day for Delhi, and 1.80 mm/day for Kolkata. The approach emphasizes explainable AI to improve understanding of precipitation patterns.
Read full article
via arXiv — cs.LG