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Found 17 papers, 8 papers with code
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.
Degradation Oriented and Regularized Network for Blind Depth Super-Resolution
Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e. g., bicubic downsampling).
Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction
MGHS projects the 2D image features into multiple subspaces, where each grid contains features within reasonable height ranges.
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design.
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements.
Scene Prior Filtering for Depth Super-Resolution
Specifically, we design an All-in-one Prior Propagation that computes the similarity between multi-modal scene priors, i. e., RGB, normal, semantic, and depth, to reduce the texture interference.
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure.
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion
To tackle these challenges, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values.
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images.
Learnable Differencing Center for Nighttime Depth Perception
Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images.
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image
Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency.
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion
Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation.
Spacecraft depth completion based on the gray image and the sparse depth map
In this way, the background interference to foreground spacecraft depth completion is effectively avoided.
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion
To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery.
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
Monocular depth estimation aims at predicting depth from a single image or video.
RigNet: Repetitive Image Guided Network for Depth Completion
However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks.
Ranked #2 on
Depth Completion
on KITTI Depth Completion
Multi-Features Guidance Network for partial-to-partial point cloud registration
In the correspondences credibility computation module, based on the conflicted relationship between the features matching matrix and the coordinates matching matrix, we score the reliability for each correspondence, which can reduce the impact of mismatched or non-matched points.
