DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth.
Code (1)
Tasks
Depth EstimationMonocular Depth EstimationSimilar Papers 제목 키워드 기반
ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE). However, they implicitly assume that depth can be recovered as a globally smooth field through iterative denoising, which …
Monocular Depth EstimationSelf-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume
Monocular depth estimation has become one of the most studied applications in computer vision, where the most accurate approaches are based on fully supervised learning models. However, the acquisition of accurate and la…
Depth EstimationMonocular Depth EstimationDAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV
Monocular depth estimation is a fundamental prerequisite for 3D reconstruction and autonomous navigation in Unmanned Aerial Vehicles (UAVs). In practical deployments, UAVs operate under highly dynamic camera poses charac…
Monocular Depth Estimation3D ReconstructionPose EstimationPPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics. However, the reliance on self-supervision introduces a strong static-scene assump…
Autonomous DrivingDecoderDepth EstimationMonocular Depth EstimationStructured Depth Prediction in Challenging Monocular Video Sequences
In this paper, we tackle the problem of estimating the depth of a scene from a monocular video sequence. In particular, we handle challenging scenarios, such as non-translational camera motion and dynamic scenes, where t…
Depth EstimationDepth PredictionMonocular Depth EstimationPrediction+1