Papers Depth Completion
“Depth Completion” 태그가 달린 논문 287편 · 필터 해제
RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completion
Dense metric depth prediction from cameras and millimeter-wave radar offers a cost-effective sensing solution for autonomous systems. However, radar measurements are inherently sparse and susceptible to clutter, multipat…
Depth CompletionVision Pretraining for Dense Spatial Perception
Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable representations from pixel observations. Modern visual foundation models te…
Depth CompletionDepth EstimationAISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior
Reliable depth perception is critical for robotic manipulation, especially for non-Lambertian objects such as transparent or highly specular surfaces, where raw depth measurements are often corrupted or missing. These fa…
Depth CompletionMotion PlanningShotcreteDepth: A Bi-modal Dataset for Robust Robotic Depth Perception in Shotcrete Construction Environments
We introduce ShotcreteDepth, a bi-modal dataset from the construction domain that captures both an active shotcreting process and general construction environments. The dataset comprises stereo RGB imagery and LiDAR poin…
Depth EstimationDepth CompletionPoint CloudsTrans2Occ: Voxel Occupancy Estimation and Grasp for Transparent Objects from Simulation to Reality
Transparent objects remain challenging for robotic perception due to unreliable depth sensing caused by refraction and reflection. While prior approaches rely on multi-view reconstruction or depth completion, they are of…
Depth CompletionRobotic GraspingTowards Consistent Video Geometry Estimation
This work presents ViGeo, a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plain transformer architecture without task-specific architec…
Depth EstimationDepth CompletionLarge Depth Completion Model from Sparse Observations
This work presents the Large Depth Completion Model (LDCM), a simple, effective, and robust framework for single-view metric depth estimation with sparse observations. Without relying on complex architectural designs, LD…
Depth EstimationDepth CompletionWideDepth: Millimeter-Accurate Benchmark for Fisheye Depth Estimation
Fisheye cameras are increasingly adopted in robotics for near-field manipulation, navigation, and immersive perception, yet indoor depth benchmarks with accurate ground truth are still missing. To address this, we introd…
Depth EstimationDepth CompletionImage GenerationThe Midas Touch for Metric Depth
Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by the absence of metric scale, local inconsistencies, and low computati…
Computational EfficiencyDepth EstimationDepth CompletionCARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography
Autonomous driving must operate across diverse surfaces to enable safe mobility. However, most driving datasets are captured on well-paved flat roads. Moreover, recent driving datasets primarily provide sparse LiDAR grou…
Autonomous Driving3D ReconstructionDepth EstimationDepth CompletionEfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion
Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing methods achieve high accuracy on standard benchmarks but rely on heav…
Depth CompletionFringe Projection Based Vision Pipeline for Autonomous Hard Drive Disassembly
Unrecovered e-waste represents a significant economic loss. Hard disk drives (HDDs) comprise a valuable e-waste stream necessitating robotic disassembly. Automating the disassembly of HDDs requires holistic 3D sensing, s…
Real-time Instance SegmentationScene UnderstandingTransfer LearningDepth CompletionToFormer: Towards Large-scale Scenario Depth Completion for Lightweight ToF Camera
Time-of-Flight (ToF) cameras possess compact design and high measurement precision to be applied to various robot tasks. However, their limited sensing range restricts deployment in large-scale scenarios. Depth completio…
Depth CompletionAURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB-LiDAR fusion methods degrade significantly under adverse weather, where both camera images and LiDAR measurements suffer from …
Scene UnderstandingDepth CompletionNeed for Speed: Zero-Shot Depth Completion with Single-Step Diffusion
We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly test-time optimization typically associated with diffusion-based method…
Domain GeneralizationDepth CompletionAny to Full: Prompting Depth Anything for Depth Completion in One Stage
Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn prio…
Monocular Depth EstimationDomain GeneralizationDepth CompletionMask-aware inference with State-Space Models
Many real-world computer vision tasks, such as depth completion, must handle inputs with arbitrarily shaped regions of missing or invalid data. For Convolutional Neural Networks (CNNs), Partial Convolutions solved this b…
Image InpaintingDepth CompletionEfficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
Zero-shot depth completion has gained attention for its ability to generalize across environments without sensor-specific datasets or retraining. However, most existing approaches rely on diffusion-based test-time optimi…
Test-time AdaptationDepth CompletionEndoDDC: Learning Sparse to Dense Reconstruction for Endoscopic Robotic Navigation via Diffusion Depth Completion
Accurate depth estimation plays a critical role in the navigation of endoscopic surgical robots, forming the foundation for 3D reconstruction and safe instrument guidance. Fine-tuning pretrained models heavily relies on …
3D ReconstructionDepth EstimationDepth CompletionDepth Completion as Parameter-Efficient Test-Time Adaptation
We introduce CAPA, a parameter-efficient test-time optimization framework that adapts pre-trained 3D foundation models (FMs) for depth completion, using sparse geometric cues. Unlike prior methods that train task-specifi…
parameter-efficient fine-tuningTest-time AdaptationDepth Completion