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 176–200 of 242 papers

TitleStatusHype
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
DidSee: Diffusion-Based Depth Completion for Material-Agnostic Robotic Perception and Manipulation—0
Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
DVMN: Dense Validity Mask Network for Depth Completion—0
Efficient Depth Completion Using Learned Bases—0
Event-Driven Dynamic Scene 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