PairGS builds a relation graph from sparse pairwise affinities on 3D Gaussians to achieve SOTA open-vocabulary segmentation with a 50x faster variant than optimization-based methods.
Gaga: Group any gaussians via 3d-aware memory bank
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot class-agnostic segmentation models. Contrasted to prior 3D scene segmentation approaches that rely on video object tracking or contrastive learning methods, Gaga utilizes spatial information and effectively associates object masks across diverse camera poses through a novel 3D-aware memory bank. By eliminating the assumption of continuous view changes in training images, Gaga demonstrates robustness to variations in camera poses, particularly beneficial for sparsely sampled images, ensuring precise mask label consistency. Furthermore, Gaga accommodates 2D segmentation masks from diverse sources and demonstrates robust performance with different open-world zero-shot class-agnostic segmentation models, significantly enhancing its versatility. Extensive qualitative and quantitative evaluations demonstrate that Gaga performs favorably against state-of-the-art methods, emphasizing its potential for real-world applications such as 3D scene understanding and manipulation.
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BEA-GS achieves superior object boundary segmentation in 3D Gaussian Splatting by introducing two new losses that adjust geometry of visible and non-visible Gaussians based on semantics.
MoGaF groups Gaussians by motion in 4D splatting representations to enable stable long-term forecasting of dynamic scenes.
GS4City derives geometry-grounded semantic masks from LoD3 CityGML models via raycasting and fuses them with 2D foundation model outputs to supervise identity encodings on Gaussians, improving coarse and fine semantic segmentation on urban datasets.
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
citing papers explorer
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Relation-Centric Open-Vocabulary 3D Gaussian Segmentation
PairGS builds a relation graph from sparse pairwise affinities on 3D Gaussians to achieve SOTA open-vocabulary segmentation with a 50x faster variant than optimization-based methods.
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BEA-GS: BEyond RAdiance Supervision in 3DGS for Precise Object Extraction
BEA-GS achieves superior object boundary segmentation in 3D Gaussian Splatting by introducing two new losses that adjust geometry of visible and non-visible Gaussians based on semantics.
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Space-Time Forecasting of Dynamic Scenes with Motion-aware Gaussian Grouping
MoGaF groups Gaussians by motion in 4D splatting representations to enable stable long-term forecasting of dynamic scenes.
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GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priors
GS4City derives geometry-grounded semantic masks from LoD3 CityGML models via raycasting and fuses them with 2D foundation model outputs to supervise identity encodings on Gaussians, improving coarse and fine semantic segmentation on urban datasets.
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A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.
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A Survey on 3D Gaussian Splatting
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.