SplatLabel: Pseudo-Labelling through 4D Gaussian Splatting

arXiv:2609.29836 · cs.CV, cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Gaussian Splatting — 19× in this paper · explained in 4 episodes
• Actor — 13× in this paper · explained in 3 episodes
• 3D Gaussian Splatting — 7× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Pseudo-labels — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• Decoupling — 3× in this paper · explained in 2 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Langevin Dynamics — 2× in this paper · explained in 2 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Stochastic Gradient Langevin Dynamics — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Segment Anything Model — 1× in this paper
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• 3D Gaussian Splatting in Robotics: A Survey
• GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians
• GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion
• OVO: Open-Vocabulary Occupancy

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models