We present WireframeDETR, our submission to the Structured Semantic 3D Reconstruction (S23DR) 2026 Challenge, which requires predicting a 3D building wireframe from multi-view COLMAP point clouds. Our method applies DETR-style set prediction directly to 3D point clouds, producing wireframes as sets of edge coordinate pairs without any intermediate vertex detection stage. We introduce three technical contributions: (1) contrastive denoising training that stabilises noisy Hungarian matching in early epochs; (2) a multi-scale encoder that aggregates the last encoder layer outputs via learned scalar weights; and (3) progressive auxiliary loss weighting that concentrates gradient signal on the decoder layers that most benefit from it. Our model achieves a public test HSS of 0.575 (F1~=~0.664, IoU~=~0.516) and a best validation HSS of 0.534 on the cleaned val split.

翻译：我们提出了WireframeDETR，这是我们对结构化语义三维重建（S23DR）2026挑战赛的提交方案。该挑战要求从多视角COLMAP点云中预测三维建筑线框。我们的方法将DETR风格的集合预测直接应用于三维点云，以边缘坐标对集合的形式生成线框，无需任何中间顶点检测阶段。我们引入了三项技术贡献：（1）对比去噪训练，用于稳定早期训练轮次中不稳定的匈牙利匹配；（2）多尺度编码器，通过学习标量权重聚合最后一个编码器层的输出；（3）渐进辅助损失加权，将梯度信号集中在从中受益最大的解码器层上。我们的模型在公开测试集上达到了0.575的HSS（F1≈0.664，IoU≈0.516），在清洗后的验证集分割上达到了0.534的最佳验证HSS。