透過分層式平衡深度重新檢視單目三維物體檢測中之深度引導方法

Abstract

單目三維物體檢測在利用深度資訊後取得了重大進展，然而，由於深度預測尚不精確，其性能表現仍然與LiDAR 方法有巨大差距。我們認為此缺陷源自於常用的基於像素的深度圖損失函數，這種損失函數先天上會使近物體與遠物體有不平衡的損失權重。為了解決這些問題，我們提出MonoHBD (Monocular Hierarchical Balanced Depth)，一個使用分層式架構的綜合的解決方案。我們結合深度分桶與深度偏移量，設計出分層式深度結構(Hierarchical Depth Map) 來提升物體的定位精度。通過使用RoIAlign，我們的平衡深度擷取器(Balanced Depth Extractor) 利用了相機內外參數以考慮幾何關係，同時捕捉了場景層級與物體層級的深度資訊。此外，我們還提出了一個嶄新的深度圖損失函數，解決了不同距離的物體不同損失權重的問題。我們提出的模型在KITTI 三維物體偵測排行榜上取得了最先進的結果，並且我們的模型支援實時檢測。我們進行了大量的消融研究已證明我們方法的有效性。

Monocular 3D object detection has seen significant advancements with the incorporation of depth information. However, there remains a considerable performance gap compared to LiDAR-based methods, largely due to inaccurate depth estimation. We argue that this issue stems from the commonly used pixel-wise depth map loss, which inherently creates the imbalance of loss weighting between near and distant objects. To address these challenges, we propose MonoHBD (Monocular Hierarchical Balanced Depth), a comprehensive solution with the hierarchical mechanism. We introduce the Hierarchical Depth Map (HDM) structure that incorporates depth bins and depth offsets to enhance the localization accuracy for objects. Leveraging RoIAlign, our Balanced Depth Extractor (BDE) module captures both scene-level depth relationships and object-specific depth characteristics while considering the geometry properties through the inclusion of camera calibration parameters. Furthermore, we propose a novel depth map loss that regularizes object-level depth features to mitigate imbalanced loss propagation. Our model reaches state-of-the-art results on the KITTI 3D object detection benchmark while supporting real-time detection. Excessive ablation studies are also conducted to prove the efficacy of our proposed modules.