BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems
Depth estimation is one of the key technologies for realizing 3D perception in unmanned systems. Monocular depth estimation has been widely researched because of its low-cost advantage, but the existing methods face the challenges of poor depth estimation performance and blurred object boundaries on embedded systems. In this paper, we propose a novel monocular depth estimation model, BoRe-Depth, which contains only 8.7M parameters. It can accurately estimate depth maps on embedded systems and significantly improves boundary quality. Firstly, we design an Enhanced Feature Adaptive Fusion Module (EFAF) which adaptively fuses depth features to enhance boundary detail representation. Secondly, we integrate semantic knowledge into the encoder to improve the object recognition and boundary perception capabilities. Finally, BoRe-Depth is deployed on NVIDIA Jetson Orin, and runs efficiently at 50.7 FPS. We demonstrate that the proposed model significantly outperforms previous lightweight models on multiple challenging datasets, and we provide detailed ablation studies for the proposed methods. The code is available at https://github.com/liangxiansheng093/BoRe-Depth.
Code (0)
등록된 구현이 없습니다.
Tasks
Monocular Depth EstimationObject RecognitionSimilar Papers 제목 키워드 기반
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes
We present a generalised self-supervised learning approach for monocular estimation of the real depth across scenes with diverse depth ranges from 1--100s of meters. Existing supervised methods for monocular depth estima…
Depth EstimationMonocular Depth EstimationSelf-Supervised LearningSelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular simultaneous localization and mapping (S…
Depth EstimationMonocular Depth EstimationRobot NavigationSelf-Supervised Learning+1Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for auton…
3D Object DetectionDepth CompletionDepth EstimationDepth Prediction+4FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume
Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame…
Depth EstimationMonocular Depth EstimationStereo Depth EstimationTwo-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
Monocular and binocular self-supervised depth estimations are two important and related tasks in computer vision, which aim to predict scene depths from single images and stereo image pairs respectively. In literature, t…
Depth EstimationMonocular Depth Estimation