ResearchResearch paperComputer Vision · Multimodal Models · Large Language Models1 source · Oct 20, 2026

OV-Foam: How 3D Representations Shape Semantic Feature Lifting

Existing feature lifting approaches for open-vocabulary segmentation rely on simple backprojection to assign features to primitives.

In the case of 3D Gaussian splatting reconstruction, the primitives overlap heavily, leading to suboptimal reconstructions, governed by many primitives per ray.
Motivated by this insight, we propose OV-Foam, which uses PowerFoam as the underlying representation.
This representation uses a power-diagram to partition the scene, resulting in zero overlap between primitives and a substantial reduction in primitives per ray, which ultimately simplifies the lifting problem and allows for closer-to-optimal feature assignment.
We show that the space partitioning primitives of OV-Foam reach near-optimal assignment, and our experiments demonstrate improved open-vocabulary segmentation over Gaussian baselines.

[1]OV-Foam: How 3D Representations Shape Semantic Feature Lifting
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 12:38 PM
Existing feature lifting approaches for open-vocabulary segmentation rely on simple backprojection to assign features to primitives.
In the case of 3D Gaussian splatting reconstruction, the primitives overlap heavily, leading to suboptimal reconstructions, governed by many primitives per ray.

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