Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2412.10444

Boundary Exploration of Next Best View Policy in 3D Robotic Scanning

Leihui Li·Lixuepiao Wan·Xuping Zhang
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Next Best View (NBV) problem is a pivotal challenge in 3D robotic scanning, with the potential to significantly improve the efficiency of object capture and reconstruction. Existing methods for determining the NBV often overlook view overlap, assume a fixed virtual origin for the camera, and rely on voxel-based representations of 3D data. To address these limitations and enhance the practicality of scanning unknown objects, we propose an NBV policy in which the next view explores the boundary of the scanned point cloud, with overlap intrinsically considered. The scanning or working distance of the camera is user-defined and remains flexible throughout the process. To this end, we first introduce a model-based approach in which candidate views are iteratively proposed based on a reference model. Scores are computed using a carefully designed strategy that accounts for both view overlap and convergence. In addition, we propose a learning-based method, the Boundary Exploration NBV Network (BENBV-Net), which predicts the NBV directly from the scanned data without requiring a reference model. BENBV-Net estimates scores for candidate boundaries, selecting the one with the highest score as the target for the next best view. It offers a significant improvement in NBV generation speed while maintaining the performance level of the model-based approach. We evaluate both methods on the ShapeNet, ModelNet, and 3D Repository datasets. Experimental results demonstrate that our approach outperforms existing methods in terms of scanning efficiency, final coverage, and overlap stability, all of which are critical for practical 3D scanning applications. The related code is available at github.com/leihui6/BENBV.

Explore related subjects

cs.CVcs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Leihui Li, Lixuepiao Wan, Xuping Zhang. 2025-07-04. Boundary Exploration of Next Best View Policy in 3D Robotic Scanning. https://arxiv.org/abs/2412.10444

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing methodologies often face significant challenges, particularly in their reliance on extensive manual efforts for cloud-free data curation and limited adaptability to diverse global conditions. In this paper, we introduce PTAViT3D, a deep learning architecture specifically designed for processing three-dimensional time series of satellite imagery from either Sentinel-1 (S1) or Sentinel-2 (S2). Additionally, we present PTAViT3D-CA, an extension of the PTAViT3D model incorporating cross-attention mechanisms to fuse S1 and S2 datasets, enhancing robustness in cloud-contaminated scenarios. The proposed methods leverage spatio-temporal correlations through a memory-efficient 3D Vision Transformer architecture, facilitating accurate boundary delineation directly from preprocessed, cloud-affected imagery. We comprehensively validate our models through extensive testing on various datasets, including Australia's ePaddocks - CSIRO's national, continental-scale agricultural field boundary product covering Australia's cropping regions - alongside public benchmarks Fields-of-the-World, PASTIS, and AI4SmallFarms. Our results consistently demonstrate state-of-the-art performance, highlighting excellent global transferability and robustness. Crucially, our approach significantly simplifies data preparation workflows by reliably processing cloud-affected imagery, thereby offering strong adaptability across diverse agricultural environments. Our code and models are publicly available at https://github.com/feevos/tfcl.

cs.CV↗
arXiv2026

Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question Answering (RSCMQA) task. Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios and inferring relationships between objects. We present a suite of global RSCMQA datasets, comprising images from 29 different regions across 14 countries. Specifically, we propose five distinct datasets, including the basic dataset RS-CMQA, the category-balanced dataset RS-CMQA-B, the high-authenticity dataset Real-RSCM, the extended dataset RS-TQA, and the extended category-balanced dataset RS-TQA-B. These datasets fill a critical gap in the field while ensuring comprehensiveness, balance, and challenging scenarios. Furthermore, we introduce a region-discrimination-guided multimodal copy-move forgery perception framework (CMFPF), which enhances the accuracy of answering questions about tampered images by leveraging prompts about the differences and connections between the source and tampered regions. Extensive experiments demonstrate that our method establishes a stronger benchmark for RSCMQA compared to general VQA and RSVQA models. Our datasets and code are publicly available at https://github.com/shenyedepisa/RSCMQA.

cs.CV↗
arXiv2026

SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection

Traffic sign detection is a challenging visual signal processing task for advanced driver assistance, where small objects, scale variation, and occlusion limit conventional detectors with fixed receptive fields. This paper proposes State-space Modeling and Dynamic Dual Fusion Network (SMDDFNet), a deep learning detector for traffic sign images. SMDDFNet integrates a Dynamic Dual Fusion (DDF) module and a state-space modeling backbone to enhance multi-scale feature representation. DDF combines efficient multi-scale attention with content-aware dynamic filtering in the frequency domain, while the backbone captures long-range dependencies with linear computational complexity. A multi-scale feature fusion neck further aggregates pyramid features for robust localization of small signs. Experiments on TT100K, GTSDB, PASCAL VOC, and the Roboflow~100 \emph{vehicle} subset show that SMDDFNet achieves competitive accuracy against recent detectors while retaining real-time throughput. The source code is available at https://github.com/rainbowyuyu/SMDDFNet

cs.CV↗