Depth Completion
76 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
SuperPrimitive: Scene Reconstruction at a Primitive Level
We address this issue with a new image representation which we call a SuperPrimitive.
MVDD: Multi-View Depth Diffusion Models
State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
In this paper, we find that the challenge of using sparse supervision for training Radar-Camera depth prediction models is the Projection Transformation Collapse (PTC).
VioLA: Aligning Videos to 2D LiDAR Scans
We study the problem of aligning a video that captures a local portion of an environment to the 2D LiDAR scan of the entire environment.
AugUndo: Scaling Up Augmentations for Unsupervised Depth Completion
The sparse depth modality have seen even less as intensity transformations alter the scale of the 3D scene, and geometric transformations may decimate the sparse points during resampling.
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior
Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view.
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data.
Fully Sparse Long Range 3D Object Detection Using Range Experts and Multimodal Virtual Points
3D object detection at long-range is crucial for ensuring the safety and efficiency of self-driving cars, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance.
Gated Cross-Attention Network for Depth Completion
With a simple yet efficient gating mechanism, our proposed method achieves fast and accurate depth completion without the need for additional branches or post-processing steps.
DEUX: Active Exploration for Learning Unsupervised Depth Perception
Training with data collected by our approach improves depth completion by an average greater than 18% across four depth completion models compared to existing exploration methods on the MP3D test set.
