Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation
Photometric consistency loss is one of the representative objective functions commonly used for self-supervised monocular depth estimation. However, this loss often causes unstable depth predictions in textureless or occluded regions due to incorrect guidance. Recent self-supervised learning approaches tackle this issue by utilizing feature representations explicitly learned from auto-encoders, expecting better discriminability than the input image. Despite the use of auto-encoded features, we observe that the method does not embed features as discriminative as auto-encoded features. In this paper, we propose residual guidance loss that enables the depth estimation network to embed the discriminative feature by transferring the discriminability of auto-encoded features. We conducted experiments on the KITTI benchmark and verified our method's superiority and orthogonality on other state-of-the-art methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationMonocular Depth EstimationSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appeara…
Depth EstimationDepth PredictionMonocular Depth EstimationRepresentation Learning+2Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation
Self-supervised monocular depth estimation has been widely studied, owing to its practical importance and recent promising improvements. However, most works suffer from limited supervision of photometric consistency, esp…
Depth EstimationMetric LearningMonocular Depth EstimationJacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video
Generating realistic human avatars in complex motions--such as clothing dynamics--requires modeling of global and local deformations which remains challenging in monocular settings. We address this problem by leveraging …
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
We present a self-supervised learning-based pipeline for dense 3D reconstruction from full-length monocular endoscopic videos without a priori modeling of anatomy or shading. Our method only relies on unlabeled monocular…
3D ReconstructionAnatomySelf-Supervised LearningTowards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation
Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics. However, since depth estimation and semantic segmentation are fu…
Depth EstimationMetric LearningMonocular Depth EstimationSemantic Segmentation+1