REVIEW 5 cited by
GS-Planner: A Gaussian-Splatting-based Planning Framework for Active High-Fidelity Reconstruction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Active reconstruction technique enables robots to autonomously collect scene data for full coverage, relieving users from tedious and time-consuming data capturing process. However, designed based on unsuitable scene representations, existing methods show unrealistic reconstruction results or the inability of online quality evaluation. Due to the recent advancements in explicit radiance field technology, online active high-fidelity reconstruction has become achievable. In this paper, we propose GS-Planner, a planning framework for active high-fidelity reconstruction using 3D Gaussian Splatting. With improvement on 3DGS to recognize unobserved regions, we evaluate the reconstruction quality and completeness of 3DGS map online to guide the robot. Then we design a sampling-based active reconstruction strategy to explore the unobserved areas and improve the reconstruction geometric and textural quality. To establish a complete robot active reconstruction system, we choose quadrotor as the robotic platform for its high agility. Then we devise a safety constraint with 3DGS to generate executable trajectories for quadrotor navigation in the 3DGS map. To validate the effectiveness of our method, we conduct extensive experiments and ablation studies in highly realistic simulation scenes.
Forward citations
Cited by 5 Pith papers
-
SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control
SplatCtrl couples real-time isotropic Gaussian scene reconstruction from RGB-D with continuous GPDF-derived SDFs inside control-barrier QP-IK for collision-free 6-DoF robot motion in dynamic environments.
-
VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting
VISTA couples a view-diversity information metric with CLIP semantics in a receding-horizon planner to improve open-vocabulary object search during online Gaussian Splatting mapping on robots.
-
Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance
A centralized multi-robot planner uses semantic segmentation and 3DGS surface uncertainty to generate reconstruction tasks, plus a global-local mode and improved K-means assignment to coordinate robots.
-
TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views
TSGaussian couples YOLOv9+SAM mask guidance with depth regularization to improve sparse-view 3D Gaussian Splatting reconstruction of specified target objects.
-
Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction
Momentum-GS improves large-scale 3D Gaussian splatting by using a momentum teacher decoder and reconstruction-guided block weighting to boost reconstruction quality and reduce memory use.
Discussion (0). Continue with ORCID to comment.