Lift, Associate, and Fuse: A Decision-Centric Framework for 2D-to-3D Foundation Model Transfer

2026-08-20

AI Summary

该论文提出LAF框架，通过五个操作解决2D到3D模型转换中的决策一致性问题，提供了一种比较现有系统和诊断不可逆故障的方法。

Abstract

Methods that transfer predictions from two-dimensional foundation models into three-dimensional segmentation are commonly grouped by task or representation. Those groupings obscure the decisions that determine whether a system remains coherent across views: where image evidence is grounded, when observations become one identity, how semantic and granularity conflicts are handled, which information is fused, and what state survives for later queries. We introduce \textbf{Lift, Associate, and Fuse (LAF)}, a decision-centric framework that represents a transfer system as five operators: \textbf{Generate, Associate, Reconcile, Fuse, and Persist/Query}. LAF defines an explicit contract for the persistent carrier---its spatial support, semantic state, identity state, uncertainty, provenance, and supported operations---and identifies the first stage at which discarded evidence becomes unrecoverable. We operationalize the framework as a structured audit protocol and apply it to 161 systems available through 7 August 2026, spanning point-, field-, Gaussian-, object-, graph-, and memory-based carriers. Representation, temporal, relational, and feed-forward stress tests required no additional analytical stage after the final confirmation pass. The resulting decision traces expose four recurring properties: association does not establish identity; carrier design fixes both the query interface and correction boundary; rendered-view, native-3D, and proposal-level evaluations are not interchangeable; and qualifiers such as \emph{training-free}, \emph{real-time}, \emph{open-vocabulary}, and \emph{generalizable} are meaningful only when attached to a stage and a complete cost ledger. LAF therefore supplies a representation-neutral method for comparing existing systems, diagnosing irreversible failures, and specifying revisable 3D perception for future agents.

Problem

• 2D-to-3D transfer
• segmentation
• foundation model
• coherence across views
• evidence grounding

Innovation

• Lift, Associate, and Fuse (LAF)
• decision-centric framework
• persistent carrier
• representation-neutral method
• revisable 3D perception