提出CoP框架，让单目3D检测自动选择预测方式，提升远距离物体精度。
Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection
- 用特征级链式传播捕捉三维属性间的关联性
- 在KITTI等数据集上深度误差显著降低，远距离物体效果更优
- 根据置信度动态切换预测模式，适合复杂遮挡场景
单目3D检测旨在仅凭单张彩色图像推断3D边界框。由于缺乏激光雷达等辅助传感器，该任务本质上是病态的，因3D到2D投影引入了深度模糊性。以往方法常并行预测3D属性（如深度、尺寸、方向），忽略了这些属性通过3D到2D投影天然相关。但简单采用顺序预测会传播误差，尤其在物体被遮挡或截断时，尺寸或方向预测不准会进一步放大深度误差。因此，平行与顺序预测均非最优。本文提出MonoCoP，一种自适应框架，通过两种互补设计学习何时及如何利用属性间相关性：链式预测（CoP）通过特征级学习、传播与聚合探索属性关联；不确定性引导选择器（GS）基于预测不确定性动态为每个物体切换至CoP或并行模式。结合两者优势，MonoCoP在KITTI、nuScenes和Waymo上达到当前最优性能，深度精度显著提升，尤其对远距离物体表现突出。
原文摘要 · Abstract (English)
Monocular 3D detection (Mono3D) aims to infer 3D bounding boxes from a single RGB image. Without auxiliary sensors such as LiDAR, this task is inherently ill-posed since the 3D-to-2D projection introduces depth ambiguity. Previous works often predict 3D attributes (e.g., depth, size, and orientation) in parallel, overlooking that these attributes are inherently correlated through the 3D-to-2D projection. However, simply enforcing such correlations through sequential prediction can propagate errors across attributes, especially when objects are occluded or truncated, where inaccurate size or orientation predictions can further amplify depth errors. Therefore, neither parallel nor sequential prediction is optimal. In this paper, we propose MonoCoP, an adaptive framework that learns when and how to leverage inter-attribute correlations with two complementary designs. A Chain-of-Prediction (CoP) explores inter-attribute correlations through feature-level learning, propagation, and aggregation, while an Uncertainty-Guided Selector (GS) dynamically switches between CoP and parallel paradigms for each object based on the predicted uncertainty. By combining their strengths, MonoCoP achieves state-of-the-art (SOTA) performance on KITTI, nuScenes, and Waymo, significantly improving depth accuracy, particularly for distant objects.
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