Depth Completion
75 papers with code • 9 benchmarks • 10 datasets
The Depth Completion task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.
Source: LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery , Unsupervised Depth Completion from Visual Inertial Odometry
Benchmarks
These leaderboards are used to track progress in Depth Completion
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Datasets
KITTI
NYUv2
Matterport3D
VOID
KITTI-Depth
TransCG
SuperCaustics
BIDCD
PLAD
Latest papers with no code
InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior
3D Gaussians have recently emerged as an efficient representation for novel view synthesis.
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects
Acquiring accurate depth information of transparent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics.
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.
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features.
VEnvision3D: A Synthetic Perception Dataset for 3D Multi-Task Model Research
Therefore, such a unique attribute can assist in exploring the potential for the multi-task model and even the foundation model without separate training methods.
Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion
This representation fails to capture the continuous depth values that conform to the real depth distribution, leading to depth smearing in boundary regions.
Test-Time Adaptation for Depth Completion
During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i. e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain.
A Concise but Effective Network for Image Guided Depth Completion in Autonomous Driving
Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth map into a dense depth prediction.
360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network
To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics.
Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion Network for Depth Completion
Depth completion is a critical task for handling depth images with missing pixels, which can negatively impact further applications.
