TL;DR
This paper proposes a method that introduces an anchor re-growing module that dynamically increases neural Gaussian density in high-gradient regions using edge-aware optimization, and the Segment Anything Model (SAM) for generating accurate object segmentation masks to guide segmentation-based loss computation.
This work proposes an Iterative Spatial Decomposition framework that bridges dense geometric priors from Multi-View Stereo (MVS) with Gaussian Splatting and introduces Hierarchical Geometric Prior Sampling (HGPS), which substantially reduce redundancy in MVS point clouds while preserving critical details, thereby provi...
3D Gaussian Splatting (3DGS) has recently gained significant attention as an efficient representation for 3D scene modeling and photo-realistic rendering. However, achieving robust instance-level segmentation within this representation remains challenging due to inter-instance interference, noisy features, and limited...
Geometry-Aware Redundancy Optimization (GARO) is proposed, a unified redundancy measurement framework in the adaptive density control stage of the traditional dynamic scene reconstruction pipeline that achieves robust trade-offs between quality and speed.
Recent advances in 3D content generation have demonstrated the effectiveness of optimization-based frameworks such as DreamGaussian, which combine 3D Gaussian Splatting (3DGS) with diffusion-guided Score Distillation Sampling (SDS) to efficiently synthesize 3D assets from a single image. Compared with earlier NeRF-base...
EffGS, a more general acceleration framework that improves training and rendering efficiency while maintaining reconstruction quality comparable to or better than vanilla 3DGS across bounded and large-scale scenes, is proposed.
This work proposes Graph-Guided Adaptive Gaussian Splatting for Scene Stylization (GAGS)—an efficient 3D scene stylization framework for Gaussian Splatting representations that can generate high-quality stylizations and outperforms existing methods both qualitatively and quantitatively.
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