arXiv Computer Vision
NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting
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arXiv AI
Aug 25
ExtrinSplat: Decoupling Geometry and Semantics for Open-Vocabulary Understanding in 3D Gaussian Splatting
ExtrinSplat is a new framework that separates geometry from semantics in 3D Gaussian Splatting scenes. It clusters Gaussians into overlapping 3D object groups and uses a Vision‑Language Model to generate lightweight textual hypotheses, creating an extrinsic index layer that handles complex polysemy. This approach reduces adaptation time from hours to minutes, cuts storage overhead by orders of magnitude, and outperforms existing embedding‑based methods on open‑vocabulary 3D object selection and semantic segmentation benchmarks.
arXiv Computer Vision
Sep 24
GaussianDS: Depth-supervised Semantic Gaussian Splatting for Scene Understanding
GaussianDS introduces a depth‑supervised framework for 3D Gaussian Splatting that jointly optimizes RGB appearance, depth, and compact semantics from scratch. By arranging multi‑view images into a pose‑aware pseudo‑video and propagating view‑consistent masks via SAM2, the method aligns semantic lifting with geometric cues, using depth supervision and edge‑aware refinement to curb semantic drift and boundary leakage. The approach achieves state‑of‑the‑art performance on LERF and 3D‑OVS benchmarks while preserving high‑fidelity reconstruction and enabling downstream tasks such as 3D object removal.
Hugging Face Trending Papers
Jul 30
MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians
Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features.
arXiv Computer Vision
4d ago
DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians
arXiv:2610.00040v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge from 2D foundation mo...
arXiv Computer Vision
Aug 25
RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions
arXiv:2508.03077v2 Announce Type: replace Abstract: Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction with...
arXiv AI
Sep 10
GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting
GoDeep is an annotation‑free method for open‑vocabulary 3D scene understanding that uses a vision‑language model solely as a translator to generate structured, entity‑level descriptions of each image. These descriptions are projected and aggregated in a language‑only embedding space, eliminating the need for a 3D training corpus or domain‑specific encoder. The approach achieves competitive performance on ScanNet++ and a cultural heritage benchmark, accurately localizes out‑of‑vocabulary objects, and offers explainable, point‑level predictions.