LabelGS assigns 2D video-tracking labels to the most-contributing 3D Gaussians, with depth-based occlusion masking and a projection filter, reporting better mIoU/PSNR than Feature-3DGS with roughly 22x faster training.
IEEE Robotics and Automation Letters 7(3), 8138–8145 (2022)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
LabelGS: Label-Aware 3D Gaussian Splatting for 3D Scene Segmentation
LabelGS assigns 2D video-tracking labels to the most-contributing 3D Gaussians, with depth-based occlusion masking and a projection filter, reporting better mIoU/PSNR than Feature-3DGS with roughly 22x faster training.