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 126–150 of 242 papers

TitleStatusHype
Deep Convolutional Compressed Sensing for LiDAR Depth Completion—0
Deep Cost Ray Fusion for Sparse Depth Video Completion—0
Deep Depth Completion from Extremely Sparse Data: A Survey—0
Depth Completion Using a View-constrained Deep Prior—0
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review—0
Deformable spatial propagation network for depth completion—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
Dense Depth Posterior (DDP) from Single Image and Sparse Range—0
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Depth Anything with Any Prior—0
Depth Coefficients for Depth Completion—0
Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints—0
Depth Completion using Piecewise Planar Model—0
Depth Completion using Plane-Residual Representation—0
Depth Completion via Deep Basis Fitting—0
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera—0
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM—0
Depth Completion with RGB Prior—0
DepthLab: From Partial to Complete—0
Depth-SIMS: Semi-Parametric Image and Depth Synthesis—0
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion—0
Deterministic Guided LiDAR Depth Map Completion—0
DEUX: Active Exploration for Learning Unsupervised Depth Perception—0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—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