Papers Monocular 3D Object Detection
“Monocular 3D Object Detection” 태그가 달린 논문 211편 · 필터 해제
Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attacks
Physical adversarial attacks on vision systems are typically studied through scene manipulation, such as adversarial patches or projections, where the adversary controls what the camera observes. Camera-side attacks usin…
Monocular 3D Object DetectionMonocular Depth EstimationMonoPRIO: Adaptive Prior Conditioning for Unified Monocular 3D Object Detection
Monocular 3D object detection remains challenging because metric size and depth are underdetermined by single-view evidence, particularly under occlusion, truncation, and projection-induced scale-depth ambiguity. Althoug…
Monocular 3D Object DetectionWildDet3D: Scaling Promptable 3D Detection in the Wild
Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--recovering the extent, location, and orientation of objects from an …
Monocular 3D Object DetectionMonoSAOD: Monocular 3D Object Detection with Sparsely Annotated Label
Monocular 3D object detection has achieved impressive performance on densely annotated datasets. However, it struggles when only a fraction of objects are labeled due to the high cost of 3D annotation. This sparsely anno…
Monocular 3D Object DetectionTowards Intrinsic-Aware Monocular 3D Object Detection
Monocular 3D object detection (Mono3D) aims to infer object locations and dimensions in 3D space from a single RGB image. Despite recent progress, existing methods remain highly sensitive to camera intrinsics and struggl…
Monocular 3D Object DetectionVirPro: Visual-referred Probabilistic Prompt Learning for Weakly-Supervised Monocular 3D Detection
Monocular 3D object detection typically relies on pseudo-labeling techniques to reduce dependency on real-world annotations. Recent advances demonstrate that deterministic linguistic cues can serve as effective auxiliary…
Monocular 3D Object DetectionSelective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detection
Monocular 3D object detection is a promising yet ill-posed task for autonomous vehicles due to the lack of accurate depth information. Cross-modality knowledge distillation could effectively transfer depth information fr…
Monocular 3D Object DetectionKnowledge DistillationAutonomous VehiclesTransfer LearningObject-Scene-Camera Decomposition and Recomposition for Data-Efficient Monocular 3D Object Detection
Monocular 3D object detection (M3OD) is intrinsically ill-posed, hence training a high-performance deep learning based M3OD model requires a humongous amount of labeled data with complicated visual variation from diverse…
Monocular 3D Object DetectionInstance-Guided Radar Depth Estimation for 3D Object Detection
Accurate depth estimation is fundamental to 3D perception in autonomous driving, supporting tasks such as detection, tracking, and motion planning. However, monocular camera-based 3D detection suffers from depth ambiguit…
Monocular 3D Object DetectionInstance SegmentationAutonomous DrivingDepth EstimationSystematic Evaluation of Depth Backbones and Semantic Cues for Monocular Pseudo-LiDAR 3D Detection
Monocular 3D object detection offers a low-cost alternative to LiDAR, yet remains less accurate due to the difficulty of estimating metric depth from a single image. We systematically evaluate how depth backbones and fea…
Monocular 3D Object DetectionInstance SegmentationFeature EngineeringLearnability-Driven Submodular Optimization for Active Roadside 3D Detection
Roadside perception datasets are typically constructed via cooperative labeling between synchronized vehicle and roadside frame pairs. However, real deployment often requires annotation of roadside-only data due to hardw…
Monocular 3D Object DetectionActive LearningMono3DV: Monocular 3D Object Detection with 3D-Aware Bipartite Matching and Variational Query DeNoising
While DETR-like architectures have demonstrated significant potential for monocular 3D object detection, they are often hindered by a critical limitation: the exclusion of 3D attributes from the bipartite matching proces…
Monocular 3D Object DetectionLeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection
Real-time monocular 3D object detection remains challenging due to severe depth ambiguity, viewpoint shifts, and the high computational cost of 3D reasoning. Existing approaches either rely on LiDAR or geometric priors t…
Monocular 3D Object DetectionVSRD++: Autolabeling for 3D Object Detection via Instance-Aware Volumetric Silhouette Rendering
Monocular 3D object detection is a fundamental yet challenging task in 3D scene understanding. Existing approaches heavily depend on supervised learning with extensive 3D annotations, which are often acquired from LiDAR …
Monocular 3D Object DetectionScene UnderstandingPoint CloudsStereoDETR: Stereo-based Transformer for 3D Object Detection
Compared to monocular 3D object detection, stereo-based 3D methods offer significantly higher accuracy but still suffer from high computational overhead and latency. The state-of-the-art stereo 3D detection method achiev…
Monocular 3D Object DetectionIDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection
Monocular 3D detection relies on just a single camera and is therefore easy to deploy. Yet, achieving reliable 3D understanding from monocular images requires substantial annotation, and 3D labels are especially costly. …
Monocular 3D Object DetectionActive LearningDifficulty-Aware Label-Guided Denoising for Monocular 3D Object Detection
Monocular 3D object detection is a cost-effective solution for applications like autonomous driving and robotics, but remains fundamentally ill-posed due to inherently ambiguous depth cues. Recent DETR-based methods atte…
Monocular 3D Object DetectionRepresentation LearningAutonomous DrivingMonoCLUE : Object-Aware Clustering Enhances Monocular 3D Object Detection
Monocular 3D object detection offers a cost-effective solution for autonomous driving but suffers from ill-posed depth and limited field of view. These constraints cause a lack of geometric cues and reduced accuracy in o…
Monocular 3D Object DetectionAutonomous DrivingSPAN: Spatial-Projection Alignment for Monocular 3D Object Detection
Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm, which employs multiple branches to estimate geometric center, depth, dimensions…
Monocular 3D Object DetectionS-LAM3D: Segmentation-Guided Monocular 3D Object Detection via Feature Space Fusion
Monocular 3D Object Detection represents a challenging Computer Vision task due to the nature of the input used, which is a single 2D image, lacking in any depth cues and placing the depth estimation problem as an ill-po…
Monocular 3D Object DetectionDepth Estimation