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
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applicationsCode1
Dense Depth Priors for Neural Radiance Fields from Sparse Input ViewsCode1
TDCNet: Transparent Objects Depth Completion with CNN-Transformer Dual-Branch Parallel NetworkCode1
Learnable Differencing Center for Nighttime Depth PerceptionCode1
TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object ManipulationCode1
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature PropagationCode1
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to EndCode1
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X DataCode1
Non-Local Spatial Propagation Network for Depth CompletionCode1
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
ClearGrasp: 3D Shape Estimation of Transparent Objects for ManipulationCode0
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping BaselineCode0
Vanishing Depth: A Depth Adapter with Positional Depth Encoding for Generalized Image EncodersCode0
Sparsity Invariant CNNsCode0
Deep RGB-D Canonical Correlation Analysis For Sparse Depth CompletionCode0
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color ImageCode0
Generating and Exploiting Probabilistic Monocular Depth EstimatesCode0
What You See Is What You Detect: Towards better Object Densification in 3D detectionCode0
SDformer: Efficient End-to-End Transformer for Depth CompletionCode0
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular CameraCode0
Deep Depth Completion of a Single RGB-D ImageCode0
Progressive Depth Decoupling and Modulating for Flexible Depth CompletionCode0
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser ObservationCode0
Propagating Confidences through CNNs for Sparse Data RegressionCode0
Radar-Camera Pixel Depth Association for Depth CompletionCode0
Sparse and noisy LiDAR completion with RGB guidance and uncertaintyCode0
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth CompletionCode0
AugUndo: Scaling Up Augmentations for Monocular Depth Completion and EstimationCode0
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth CompletionCode0
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and ReportCode0
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer VisionCode0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth CompletionCode0
Prior based Sampling for Adaptive LiDARCode0
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
Adaptive LiDAR Sampling and Depth Completion using Ensemble VarianceCode0
Masked Spatial Propagation Network for Sparsity-Adaptive Depth RefinementCode0
Confidence Propagation through CNNs for Guided Sparse Depth RegressionCode0
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous VehiclesCode0
Unsupervised Depth Completion from Visual Inertial OdometryCode0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Indoor Depth Completion with Boundary Consistency and Self-AttentionCode0
In Defense of Classical Image Processing: Fast Depth Completion on the CPUCode0
Learning Guided Convolutional Network for Depth CompletionCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
Sparse and noisy LiDAR completion with RGB guidance anduncertaintyCode0
Depth Completion via Deep Basis Fitting—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