UniDet3D：多數據集室內3D物體檢測

UniDet3D: Multi-dataset Indoor 3D Object Detection

September 6, 2024

作者: Maksim Kolodiazhnyi, Anna Vorontsova, Matvey Skripkin, Danila Rukhovich, Anton Konushin

cs.AI

摘要

隨著客戶對機器人技術和擴增實境智能解決方案的需求不斷增長，對從點雲中進行的3D物體檢測引起了相當大的關注。然而，現有的室內數據集單獨採集的數據量太小，多樣性不足，無法訓練出功能強大且通用的3D物體檢測模型。與此同時，更通用的方法利用基礎模型仍然在質量上不如基於特定任務的監督式訓練。在這項工作中，我們提出了一種簡單而有效的3D物體檢測模型，該模型是通過混合室內數據集進行訓練的，能夠在各種室內環境中運作。通過統一不同的標籤空間，使得能夠通過監督聯合訓練方案在多個數據集上學習強大的表示。所提出的網絡架構基於基本的Transformer編碼器構建，使其易於運行、自定義和擴展預測管道以實現實際應用。大量實驗表明，在6個室內基準測試中，相對於現有的3D物體檢測方法，取得了顯著的進展：ScanNet（+1.1 mAP50）、ARKitScenes（+19.4 mAP25）、S3DIS（+9.1 mAP50）、MultiScan（+9.3 mAP50）、3RScan（+3.2 mAP50）和ScanNet++（+2.7 mAP50）。代碼可在 https://github.com/filapro/unidet3d 找到。

English

Growing customer demand for smart solutions in robotics and augmented reality has attracted considerable attention to 3D object detection from point clouds. Yet, existing indoor datasets taken individually are too small and insufficiently diverse to train a powerful and general 3D object detection model. In the meantime, more general approaches utilizing foundation models are still inferior in quality to those based on supervised training for a specific task. In this work, we propose , a simple yet effective 3D object detection model, which is trained on a mixture of indoor datasets and is capable of working in various indoor environments. By unifying different label spaces, enables learning a strong representation across multiple datasets through a supervised joint training scheme. The proposed network architecture is built upon a vanilla transformer encoder, making it easy to run, customize and extend the prediction pipeline for practical use. Extensive experiments demonstrate that obtains significant gains over existing 3D object detection methods in 6 indoor benchmarks: ScanNet (+1.1 mAP50), ARKitScenes (+19.4 mAP25), S3DIS (+9.1 mAP50), MultiScan (+9.3 mAP50), 3RScan (+3.2 mAP50), and ScanNet++ (+2.7 mAP50). Code is available at https://github.com/filapro/unidet3d .