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 end this, 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, to infer richer panoptic features from occupied voxels, 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.

本研究针对现有单目视频全景3D重建方法在推理速度和准确性方面的低效问题，提出了EPRecon框架。该框架通过引入轻量级模块直接估计3D体积中的场景深度优先信息，并从体素特征和图像特征提取更丰富的全景特征，从而显著提升重建质量和分割精度。实验表明，EPRecon在全景3D重建质量和实时推理方面优于目前最先进的方法。

EPRecon: 一种高效的单目视频实时全景3D重建框架