Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment
The current neural implicit SLAM methods have demonstrated excellent performance in reconstructing ideal static 3D scenes. However, it remains a significant challenge for these methods to handle real scenes with drastic changes in lighting conditions and dynamic environments. This paper proposes a n...
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MDPI AG
2025-03-01
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| author | Yuquan Zhang Guosheng Feng |
| author_facet | Yuquan Zhang Guosheng Feng |
| author_sort | Yuquan Zhang |
| collection | DOAJ |
| description | The current neural implicit SLAM methods have demonstrated excellent performance in reconstructing ideal static 3D scenes. However, it remains a significant challenge for these methods to handle real scenes with drastic changes in lighting conditions and dynamic environments. This paper proposes a neural implicit SLAM method that effectively deals with dynamic scenes. We employ a keyframe selection and tracking switching approach based on Lucas–Kanade (LK) optical flow, which serves as prior construction for the Conditional Random Fields potential function. This forms a semantic-based joint estimation method for dynamic and static pixels and constructs corresponding loss functions to impose constraints on dynamic scenes. We conduct experiments on various dynamic and challenging scene datasets, including TUM RGB-D, Openloris, and Bonn. The results demonstrate that our method significantly outperforms existing neural implicit SLAM systems in terms of reconstruction quality and tracking accuracy. |
| format | Article |
| id | doaj-art-0dcaed798afa4b4483959d3b9b89c607 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0dcaed798afa4b4483959d3b9b89c6072025-08-20T01:48:46ZengMDPI AGSensors1424-82202025-03-01256167910.3390/s25061679Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle AdjustmentYuquan Zhang0Guosheng Feng1School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaThe current neural implicit SLAM methods have demonstrated excellent performance in reconstructing ideal static 3D scenes. However, it remains a significant challenge for these methods to handle real scenes with drastic changes in lighting conditions and dynamic environments. This paper proposes a neural implicit SLAM method that effectively deals with dynamic scenes. We employ a keyframe selection and tracking switching approach based on Lucas–Kanade (LK) optical flow, which serves as prior construction for the Conditional Random Fields potential function. This forms a semantic-based joint estimation method for dynamic and static pixels and constructs corresponding loss functions to impose constraints on dynamic scenes. We conduct experiments on various dynamic and challenging scene datasets, including TUM RGB-D, Openloris, and Bonn. The results demonstrate that our method significantly outperforms existing neural implicit SLAM systems in terms of reconstruction quality and tracking accuracy.https://www.mdpi.com/1424-8220/25/6/1679Dense SLAMneural implicit codingsurface rendering |
| spellingShingle | Yuquan Zhang Guosheng Feng Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment Sensors Dense SLAM neural implicit coding surface rendering |
| title | Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment |
| title_full | Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment |
| title_fullStr | Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment |
| title_full_unstemmed | Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment |
| title_short | Neural Radiance Field Dynamic Scene SLAM Based on Ray Segmentation and Bundle Adjustment |
| title_sort | neural radiance field dynamic scene slam based on ray segmentation and bundle adjustment |
| topic | Dense SLAM neural implicit coding surface rendering |
| url | https://www.mdpi.com/1424-8220/25/6/1679 |
| work_keys_str_mv | AT yuquanzhang neuralradiancefielddynamicsceneslambasedonraysegmentationandbundleadjustment AT guoshengfeng neuralradiancefielddynamicsceneslambasedonraysegmentationandbundleadjustment |