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NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

Training-free weighted aggregation is widely used to lift 2D semantic features onto 3D Gaussians for open-vocabulary scene understanding, yet its theoretical role remains insufficiently understood. Existing analyses typically justify this operation from the rendering side, treating Gaussian features as linearly composable Euclidean variables for reconstructing 2D feature maps. However, this view d

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#21 most recent of 237 cs.CV papers we have recorded · ↑ newer: PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Singl · ↓ older: Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware V
Cite this page: NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting: the #21 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/normlift-from-lifted-features-to-semantic-reliability-in-3d-gaussian-splatting.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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