← 研踪 PaperTrailCVF · CVPR 2026:cvf:0366dc79c8b78b56cdc037765b3dff0b · 记录 #1
CompetitorFormer: Mitigating Query Conflicts for 3D Instance Segmentation via Competitive Strategy
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:27
摘要
Transformer-based approaches have recently become the dominant paradigm for 3D instance segmentation. These methods typically employ a multi-layer decoder that iteratively refines a set of learnable queries into instance mask predictions. However, we observe that multiple queries often target the same instance simultaneously, leading to fragmented masks for a single object. We define this phenomenon as inter-query competition, which slows convergence and limits segmentation accuracy. To address this problem, we present CompetitorFormer, a novel framework designed for Transformer-based methods. Our method mitigates inter-query competition by explicitly modeling the competitive relationships among queries. Specifically, we introduce a Query Competition Layer before each decoder stage to construct a dynamic competitive landscape, allowing each query to perceive its relative importance. In addition, the proposed Relative Relationship Encoding and Rank Cross-Attention modules enhance both self-attention and cross-attention by prioritizing dominant queries. Extensive experiments show that our approach converges faster and achieves superior performance on the ScanNetV2, ScanNet++V2, ScanNet200, and S3DIS datasets. Code is available at https://github.com/DuanchuWang/CompetitorFormer.
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记录 #1 · 2026 · 最新观测版本
CompetitorFormer: Mitigating Query Conflicts for 3D Instance Segmentation via Competitive Strategy
Transformer-based approaches have recently become the dominant paradigm for 3D instance segmentation. These methods typically employ a multi-layer decoder that iteratively refines a set of learnable queries into instance mask predictions. However, we observe that multiple queries often target the same instance simultaneously, leading to fragmented masks for a single object. We define this phenomenon as inter-query competition, which slows convergence and limits segmentation accuracy. To address this problem, we present CompetitorFormer, a novel framework designed for Transformer-based methods. Our method mitigates inter-query competition by explicitly modeling the competitive relationships among queries. Specifically, we introduce a Query Competition Layer before each decoder stage to construct a dynamic competitive landscape, allowing each query to perceive its relative importance. In addition, the proposed Relative Relationship Encoding and Rank Cross-Attention modules enhance both self-attention and cross-attention by prioritizing dominant queries. Extensive experiments show that our approach converges faster and achieves superior performance on the ScanNetV2, ScanNet++V2, ScanNet200, and S3DIS datasets. Code is available at https://github.com/DuanchuWang/CompetitorFormer.
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