Problem
Research questions and friction points this paper is trying to address.
Language-based 3D localization
global descriptors
object-level semantics
spatial coherence
PosEviLoc: Position-Conditioned Spatial Evidence for Language-Based 3D Localization
🤖 AI Summary
本文提出PosEviLoc，通过显式的语义和空间证据评估每个候选子图，解决基于语言的3D定位问题，优于现有方法。
📝 Abstract
Language-based 3D localization retrieves the point-cloud submap containing a target position from descriptions of nearby objects and their spatial relations. Existing methods typically compress queries and submaps into global descriptors, potentially obscuring object-level semantics and cross-description spatial coherence. We propose Position-Conditioned Evidence Localization (PosEviLoc), a query-position-aware framework for coarse text-to-point-cloud localization. Instead of relying on global matching, PosEviLoc evaluates each candidate submap using explicit semantic and spatial evidence. It models direction as a relation jointly determined by an object position and a hypothetical query position. The resulting Query-Position Spatial Evidence Field (QSEF) measures the fraction of query descriptions supported at each hypothetical position, explicitly capturing their agreement without using the ground-truth query pose to construct the evidence field. A Multi-Level Evidence Readout (MER) summarizes this evidence in a compact representation, which a lightweight MLP converts into a retrieval score. Across five benchmarks, PosEviLoc outperforms MNCL by an average of 17 percentage points in Recall@1. When used as a plug-and-play reranker, it improves MNCL by an average of 16 percentage points. Moreover, PosEviLoc introduces substantially fewer parameters and achieves faster inference speed than existing methods.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Position-Conditioned Evidence Localization
Query-Position Spatial Evidence Field
Multi-Level Evidence Readout
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