Papers Weakly Supervised 3D Detection
“Weakly Supervised 3D Detection” 태그가 달린 논문 6편 · 필터 해제
Weak Cube R-CNN: Weakly Supervised 3D Detection using only 2D Bounding Boxes
Monocular 3D object detection is an essential task in computer vision, and it has several applications in robotics and virtual reality. However, 3D object detectors are typically trained in a fully supervised way, relyin…
3D Object DetectionMonocular 3D Object Detectionobject-detectionObject Detection+1SC3D: Label-Efficient Outdoor 3D Object Detection via Single Click Annotation
LiDAR-based outdoor 3D object detection has received widespread attention. However, training 3D detectors from the LiDAR point cloud typically relies on expensive bounding box annotations. This paper presents SC3D, an in…
3D Object Detectionobject-detectionObject DetectionPseudo Label+1InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
Perception systems of autonomous vehicles are susceptible to occlusion, especially when examined from a vehicle-centric perspective. Such occlusion can lead to overlooked object detections, e.g., larger vehicles such as …
3D Multi-Object Tracking3D Object DetectionWeakly Supervised 3D DetectionVSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abund…
3D Object DetectionDepth EstimationMonocular 3D Object DetectionMonocular Depth Estimation+5WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection methods highly rely on training with the man…
3D Object DetectionMonocular 3D Object DetectionObjectobject-detection+3Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves an ill-posed inverse problem by considerin…
Weakly Supervised 3D Detection