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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 242 papers

TitleStatusHype
GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNsCode1
Unsupervised Depth Completion with Calibrated Backprojection LayersCode1
Dense Depth Priors for Neural Radiance Fields from Sparse Input ViewsCode1
LiDARTouch: Monocular metric depth estimation with a few-beam LiDARCode1
RGB-D Local Implicit Function for Depth Completion of Transparent ObjectsCode1
SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective FusionCode1
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature PropagationCode1
Learning an Efficient Multimodal Depth Completion ModelCode1
Depth Completion using Geometry-Aware EmbeddingCode1
A Surface Geometry Model for LiDAR Depth CompletionCode1
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
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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