DPGS-SLAM: Diffusion-Prior and Provenance-Aware Monocular Gaussian Splatting SLAM for Dynamic Visual Sensing
Overview
This paper presents DPGS-SLAM, a monocular RGB SLAM framework designed for dense mapping in environments with dynamic objects.
Generated from attributed reports · Updated 7 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · average ATE RMSE values: 1.2 other · Basis not reported · Differing source assertions
“al RGB observations and predicted depth. Experimental results demonstrated competitive camera tracking, with average ATE RMSE values of 1.2 cm on TUM RGB-D and 2.7 cm on BONN RGB-D. On StaDy4D, DPGS-SLAM improved average PSNR by 4.60 dB over WildGS-SLAM using only monocular RGB input.”
Exact source · revision 1Source owner not reported
Reported quantity · average ATE RMSE values: 2.7 other · Basis not reported · Differing source assertions
“al RGB observations and predicted depth. Experimental results demonstrated competitive camera tracking, with average ATE RMSE values of 1.2 cm on TUM RGB-D and 2.7 cm on BONN RGB-D. On StaDy4D, DPGS-SLAM improved average PSNR by 4.60 dB over WildGS-SLAM using only monocular RGB input.”
Exact source · revision 1Source owner not reported
Reported quantity · average PSNR improvement: 4.6 other · Basis not reported
“al RGB observations and predicted depth. Experimental results demonstrated competitive camera tracking, with average ATE RMSE values of 1.2 cm on TUM RGB-D and 2.7 cm on BONN RGB-D. On StaDy4D, DPGS-SLAM improved average PSNR by 4.60 dB over WildGS-SLAM using only monocular RGB input.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDPGS-SLAM: Diffusion-Prior and Provenance-Aware Monocular Gaussian Splatting SLAM for Dynamic Visual Sensing
This paper presents DPGS-SLAM, a monocular RGB SLAM framework designed for dense mapping in environments with dynamic objects.
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