REVIEW 13 cited by
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
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
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
read the original abstract
We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation. Our approach enables interactive-time reconstruction and photo-realistic rendering from real-world single-camera RGBD videos. To this end, we propose a novel effective strategy for seeding new Gaussians for newly explored areas and their effective online optimization that is independent of the scene size and thus scalable to larger scenes. This is achieved by organizing the scene into sub-maps which are independently optimized and do not need to be kept in memory. We further accomplish frame-to-model camera tracking by minimizing photometric and geometric losses between the input and rendered frames. The Gaussian representation allows for high-quality photo-realistic real-time rendering of real-world scenes. Evaluation on synthetic and real-world datasets demonstrates competitive or superior performance in mapping, tracking, and rendering compared to existing neural dense SLAM methods.
Forward citations
Cited by 13 Pith papers
-
MAGS-SLAM: Monocular Multi-Agent Gaussian Splatting SLAM for Geometrically and Photometrically Consistent Reconstruction
MAGS-SLAM is the first RGB-only multi-agent 3D Gaussian Splatting SLAM framework that matches RGB-D performance via compact submap sharing, geometry-appearance loop verification, and occupancy-aware fusion.
-
GaussLite: Online Task-Conditioned 3D Gaussian Splatting for Real-Time Robotic Mapping
GaussLite conditions 3D Gaussian Splatting seeding density, gradient flow, and scaling on task relevance masks derived from LLM-parsed natural language and open-vocabulary detection, yielding +2.72 dB ROI PSNR gains o...
-
CoMo3R-SLAM: Collaborative Monocular Dense SLAM with Learned 3D Reconstruction Priors for Outdoor Multi-Agent Systems
Presents CoMo3R-SLAM, the first collaborative monocular dense RGB SLAM using learned feed-forward 3D priors for outdoor multi-agent systems, achieving competitive accuracy and global consistency without depth sensors ...
-
WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment
WaterSplat-SLAM achieves robust camera tracking and high-fidelity rendering in underwater environments by coupling semantic medium filtering into two-view reconstruction and using an online medium-aware Gaussian map.
-
GeoGS-SLAM: Geometry-Only Gaussian Splatting for Dense Monocular SLAM
GeoGS-SLAM removes appearance parameters from 3D Gaussian Splatting for geometry-only dense monocular SLAM, achieving faster convergence and fewer primitives while introducing a coherent Sim(3) map update for loop closure.
-
Why does Deep Learning Improve Visual SLAM?
Learned 2D data association and uncertainty—not recurrent architectures—drive the performance gains of deep visual SLAM, as shown by integrating them into classical ORB-SLAM3.
-
NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation
NG-GS uses NeRF guidance and RBF interpolation on 3DGS to produce smoother, higher-quality object segmentation boundaries.
-
Compact 3D Gaussian Splatting For Dense Visual SLAM
A compact 3D Gaussian Splatting SLAM system reduces Gaussian count and parameter size via masking and a geometry codebook while preserving SOTA reconstruction quality and pose accuracy.
-
Robust and Efficient Monocular 3D Gaussian SLAM for Kilometer-Scale Outdoor Scenes
KiloGS-SLAM is a monocular 3DGS SLAM system with condition-triggered hybrid tracking and probabilistic chunk-based Gaussian mapping that scales to over 10,000 frames in outdoor environments while maintaining accuracy ...
-
Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping
Mono-Hydra++ is a monocular RGB-IMU pipeline that constructs hierarchical 3D scene graphs in real time while reporting lower trajectory error than some RGB-D baselines on indoor datasets.
-
SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors
SING3R-SLAM adds submap-level global alignment and reconstruction priors to a Gaussian map to reduce drift and improve local geometry in monocular indoor SLAM.
-
VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM
VCS-SLAM introduces geometric validation of semantic observations via visibility consistency, boundary evidence, and ray uncertainty to improve fusion in 3D Gaussian SLAM.
-
A Survey on 3D Gaussian Splatting
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.