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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.01807 |
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| _version_ | 1866929507474079744 |
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| author | Zhou, Zhen Ma, Yunkai Fan, Junfeng Zhang, Shaolin Jing, Fengshui Tan, Min |
| author_facet | Zhou, Zhen Ma, Yunkai Fan, Junfeng Zhang, Shaolin Jing, Fengshui Tan, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular Video Zhou, Zhen Ma, Yunkai Fan, Junfeng Zhang, Shaolin Jing, Fengshui Tan, Min Computer Vision and Pattern Recognition 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. |
| title | EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.01807 |