SOTAVerified

Depth Completion

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

Papers

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Showing 51–100 of 242 papers

TitleStatusHype
An Adaptive Framework for Learning Unsupervised Depth CompletionCode1
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth CompletionCode1
Learning Topology from Synthetic Data for Unsupervised Depth CompletionCode1
Scene Completeness-Aware Lidar Depth Completion for Driving ScenarioCode1
LiDARTouch: Monocular metric depth estimation with a few-beam LiDARCode1
PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and ConsistencyCode1
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature PropagationCode1
NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance FieldsCode1
SteeredMarigold: Steering Diffusion Towards Depth Completion of Largely Incomplete Depth MapsCode1
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to EndCode1
Depth Completion via Deep Basis Fitting—0
Depth Completion using Plane-Residual Representation—0
Depth Completion using Piecewise Planar Model—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
All-day Depth Completion via Thermal-LiDAR Fusion—0
Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints—0
Depth Coefficients for Depth Completion—0
Depth Anything with Any Prior—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network—0
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Dense Depth Posterior (DDP) from Single Image and Sparse Range—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
All-day Depth Completion—0
Deformable spatial propagation network for depth completion—0
BIDCD -- Bosch Industrial Depth Completion Dataset—0
Enabling 3D Object Detection with a Low-Resolution LiDAR—0
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion—0
GAC-Net_Geometric and attention-based Network for Depth Completion—0
Aggregating Feature Point Cloud for Depth Completion—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
Learning Joint 2D-3D Representations for Depth Completion—0
Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion—0
Depth Completion Using a View-constrained Deep Prior—0
From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction—0
Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities—0
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty—0
Flexible Depth Completion for Sparse and Varying Point Densities—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
Deep Depth Completion from Extremely Sparse Data: A Survey—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—0
Deep Cost Ray Fusion for Sparse Depth Video Completion—0
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review—0
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects—0
GuideFormer: Transformers for Image Guided Depth Completion—0
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion—0
Deep Convolutional Compressed Sensing for LiDAR Depth Completion—0
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All datasetsKITTI Depth CompletionVOIDNYU-Depth V2Matterport3DKITTIKITTI Depth Completion 500 pointsKITTI Depth Completion Eigen SplitPLADVOID-150

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SparseConvsRMSE1,601—Unverified
2NConv-CNNRMSE1,268—Unverified
3VOICEDRMSE1,169.97—Unverified
4ScaffNet-FusionNetRMSE1,121.93—Unverified
5KBNetRMSE1,069.47—Unverified
6Spade-sDRMSE1,035—Unverified
7HMS-NetRMSE937—Unverified
8Spade-RGBsDRMSE918—Unverified
9NConv-CNN-L1RMSE859—Unverified
10NConv-CNN-L2RMSE830—Unverified
#ModelMetricClaimedVerifiedStatus
1SS-S2DMAE178.85—Unverified
2DDPMAE151.86—Unverified
3VOICEDMAE85.05—Unverified
4ScaffNet-FusionNetMAE59.53—Unverified
5KBNetMAE39.8—Unverified
6NLSPNMAE26.74—Unverified
#ModelMetricClaimedVerifiedStatus
1Struct-MDCRMSE0.14—Unverified
2DSNRMSE0.1—Unverified
3NLSPNRMSE0.09—Unverified
#ModelMetricClaimedVerifiedStatus
1SA+SSIM+BCRMSE1.09—Unverified
2DM-LRN-b4RMSE1—Unverified
#ModelMetricClaimedVerifiedStatus
1FusionDepthRMSE1,193.92—Unverified
#ModelMetricClaimedVerifiedStatus
1CFCNetRMSE 2.96—Unverified
#ModelMetricClaimedVerifiedStatus
1DSNREL0.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Struct-MDCMAE1,170.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Struct-MDCMAE111.33—Unverified