REVIEW 8 cited by
Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images
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
Signed reviews
read the original abstract
In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a small number of images are available. To address this issue, we introduce a dense depth map as a geometry guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular depth estimation model and aligning the scale and offset using sparse COLMAP feature points. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting, mitigating floating artifacts, and ensuring adherence to geometric constraints. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images. Our approach demonstrates robust geometry compared to the original method that relies solely on images. Project page: robot0321.github.io/DepthRegGS
Forward citations
Cited by 8 Pith papers
-
4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans
A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.
-
You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
YOGO reformulates stochastic 3D Gaussian Splatting into a deterministic budget-aware system and supplies an ultra-dense dataset to enforce physical fidelity over viewpoint interpolation.
-
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering
RDG-GS combines refined monocular depth priors, a relative depth similarity loss, and adaptive point densification to improve sparse-view 3D Gaussian Splatting rendering.
-
4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization
A 4D Gaussian Splatting method with uncertainty-weighted diffusion and depth-smoothness regularization plus dynamic-region densification improves reconstruction and novel-view synthesis on casually recorded monocular videos.
-
VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal
VEIGAR is a pipeline for 3D object removal in Gaussian Splatting that uses deep stereo depth projection and a scale-invariant depth loss to achieve faster training and comparable quality to prior state-of-the-art.
-
FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistency
FatesGS combines local monocular depth ranking, depth smoothing, and multi-view feature alignment in a 2D Gaussian splatting pipeline to obtain accurate surface meshes from only three views without dataset-scale pre-training.
-
Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions
Puzzle Similarity detects artifacts in novel views of 3D scenes by max-pooling feature similarity against training views, and it outperforms prior quality metrics in correlating with human artifact segmentations.
-
Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction
NDF treats a fixed-image depth estimator as an implicit field and optimizes it on observed depth at test time, improving inpainting accuracy and cross-view consistency.
Discussion (0). Continue with ORCID to comment.