SGP-Net: semantically-guided multi-task refinement for monocular depth estimation in scenes with potentially movable object categories

Engineering notes

Key topics: autonomous driving, depth estimation, monocular depth, kitti, foundation model, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract Monocular depth estimation (MDE) is a fundamental task for 3D scene understanding and provides an efficient perception solution for autonomous driving, robotic navigation, augmented reality, and other intelligent systems. Despite recent progress in supervised models and large-scale depth foundation models, accurate MDE in scenes containing potentially movable object categories remains challenging, since such foreground categories often introduce feature entanglement with static background regions and blurred object boundaries. In this paper, "dynamic" refers to potentially dynamic semantic-category regions, such as vehicles and pedestrians, derived from semantic labels or pseudo-labels; it does not denote explicit frame-level motion estimated from optical flow, tracking, or multi-frame input. To address these challenges, we propose SGP-Net, a semantically guided progressive refinement framework for MDE based on multi-task learning. SGP-Net is built upon DCDepth and extends it with semantic-aware branches and dynamic-category constraints. SGP-Net consists of two key components. First, a Semantically-Guided Pyramid Feature Fusion (SG-PFF) module uses pixel-level semantic priors to reweight multi-scale depth features, thereby preserving object contours and enhancing geometric perception in dynamic-category regions. Second, a dynamically weighted multi-constraint loss with a phased weight scheduling strategy is designed to balance scale-invariant and edge-aware supervision during training while alleviating negative transfer between tasks. Experiments on KITTI, NYU-Depth-v2, and TOFDC show that SGP-Net improves key relative-error and accuracy metrics over the DCDepth baseline and remains competitive with recent methods. On KITTI dynamic-category pixels, SGP-Net reduces AbsRel from 0.0477 to 0.0448 and improves delta_1 from 0.9786 to 0.9862. Qualitative results and ablation studies further verify the effectiveness of semantic-guided refinement and dynamic-category supervision in regions belonging to potentially movable categories.

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