We study 3D shape modeling from a single image and make contributions to it in three aspects.
Ranked #1 on 3D Shape Classification on Pix3D
Object viewpoint estimation from 2D images is an essential task in computer vision.
Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets.
In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box.
Most deep pose estimation methods need to be trained for specific object instances or categories.
We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation.
In this paper, we tackle the problems of few-shot object detection and few-shot viewpoint estimation.
Ranked #1 on Few-Shot Object Detection on MS-COCO (10-shot)
In this work, we propose a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation.
Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.