EPRecon：一种用于单目视频实时全景三维重建的高效框架
EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular Video
AI总结：
EPRecon提出轻量级深度先验估计模块和双特征提取策略，实现单目视频实时高质量全景三维重建，在ScanNetV2上超越现有方法。
摘要：
从单目视频进行全景三维重建是机器人场景理解中的一项基本感知任务。然而，现有方法在推理速度和精度方面效率低下，限制了其实际应用。我们提出了EPRecon，一种高效的实时全景三维重建框架。当前基于体积的重建方法通常利用多视图深度图融合来获取场景深度先验，这耗时且对实时场景重建构成挑战。为解决此问题，我们提出一个轻量级模块，直接在三维体积中估计场景深度先验，通过生成所有体素的占用概率来提升重建质量。此外，与现有全景分割方法相比，EPRecon从体素特征和对应图像特征中提取全景特征，获得更详细和全面的实例级语义信息，实现更准确的分割结果。在ScanNetV2数据集上的实验结果表明，EPRecon在全景三维重建质量和实时推理方面均优于当前最先进的方法。代码可在https://github.com/zhen6618/EPRecon获取。
英文摘要：
Panoptic 3D reconstruction from a monocular video is a fundamental perceptual task in robotic scene understanding. However, existing efforts suffer from inefficiency in terms of inference speed and accuracy, limiting their practical applicability. We present EPRecon, an efficient real-time panoptic 3D reconstruction framework. Current volumetric-based reconstruction methods usually utilize multi-view depth map fusion to obtain scene depth priors, which is time-consuming and poses challenges to real-time scene reconstruction. To address this issue, we propose a lightweight module to directly estimate scene depth priors in a 3D volume for reconstruction quality improvement by generating occupancy probabilities of all voxels. In addition, compared with existing panoptic segmentation methods, EPRecon extracts panoptic features from both voxel features and corresponding image features, obtaining more detailed and comprehensive instance-level semantic information and achieving more accurate segmentation results. Experimental results on the ScanNetV2 dataset demonstrate the superiority of EPRecon over current state-of-the-art methods in terms of both panoptic 3D reconstruction quality and real-time inference. Code is available at https://github.com/zhen6618/EPRecon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
