Topo-R1: Detecting Topological Anomalies via Vision-Language Models

arXiv cs.CV / 3/16/2026

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Key Points

The paper reframes topological correctness as topological anomaly detection in segmentation masks for tubular structures, enabling annotation-free detection across domains.
It shows that state-of-the-art Vision-Language Models perform nearly at random on topology-aware tasks, highlighting limitations in detecting sparse connectivity errors in dense structures.
The authors introduce an automated data-curation pipeline that synthesizes diverse topological anomalies with verifiable annotations across progressively harder levels to build the first large-scale, multi-domain benchmark for this task.
They propose Topo-R1, a two-stage training framework consisting of supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO), and a topology-aware composite reward integrating type-aware Hungarian matching, spatial localization scoring, and a centerline Dice (clDice) reward.
Extensive experiments demonstrate that Topo-R1 establishes a new annotation-free paradigm for topological quality assessment and consistently outperforms general-purpose VLMs and supervised baselines across evaluation protocols.

Abstract

Topological correctness is crucial for tubular structures such as blood vessels, nerve fibers, and road networks. Existing topology-preserving methods rely on domain-specific ground truth, which is costly and rarely transfers across domains. When deployed to a new domain without annotations, a key question arises: how can we detect topological anomalies without ground-truth supervision? We reframe this as topological anomaly detection, a structured visual reasoning task requiring a model to locate and classify topological errors in predicted segmentation masks. Vision-Language Models (VLMs) are natural candidates; however, we find that state-of-the-art VLMs perform nearly at random, lacking the fine-grained, topology-aware perception needed to identify sparse connectivity errors in dense structures. To bridge this gap, we develop an automated data-curation pipeline that synthesizes diverse topological anomalies with verifiable annotations across progressively difficult levels, thereby constructing the first large-scale, multi-domain benchmark for this task. We then introduce Topo-R1, a framework that endows VLMs with topology-aware perception via two-stage training: supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO). Central to our approach is a topology-aware composite reward that integrates type-aware Hungarian matching for structured error classification, spatial localization scoring, and a centerline Dice (clDice) reward that directly penalizes connectivity disruptions, thereby jointly incentivizing semantic precision and structural fidelity. Extensive experiments demonstrate that Topo-R1 establishes a new paradigm for annotation-free topological quality assessment, consistently outperforming general-purpose VLMs and supervised baselines across all evaluation protocols.