Analyzing 3D Objects in Cluttered Images
We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on the application of finding and analyzing cars. We do so with a two-stage model; the first stage reasons about 2D shape and appearance variation due to within-class variation(station wagons look different than sedans) and changes in viewpoint. Rather than using a view-based model, we describe a compositional representation that models a large number of effective views and shapes using a small number of local view-based templates. We use this model to propose candidate detections and 2D estimates of shape. These estimates are then refined by our second stage, using an explicit 3D model of shape and viewpoint. We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint. We learn all model parameters from 2D annotations. We demonstrate state-of-the-art accuracy for detection, viewpoint estimation, and 3D shape reconstruction on challenging images from the PASCAL VOC 2011 dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Shape ReconstructionViewpoint EstimationSimilar Papers 제목 키워드 기반
Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images
Recent methods for 6D pose estimation of objects assume either textured 3D models or real images that cover the entire range of target poses. However, it is difficult to obtain textured 3D models and annotate the poses o…
6D Pose EstimationPose EstimationReinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but pose estimation is difficult on clutter…
Pose Estimationreinforcement-learningSCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from…
object-detectionObject DetectionObject Detection In Aerial ImagesSCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detectors often routinely locate the objects in …
Denoisingobject-detectionObject DetectionObject Detection In Aerial ImagesCategory-level Shape Estimation for Densely Cluttered Objects
Accurately estimating the shape of objects in dense clutters makes important contribution to robotic packing, because the optimal object arrangement requires the robot planner to acquire shape information of all existed …
Instance SegmentationObjectPoint cloud reconstructionSegmentation+1