Deep learning techniques for point cloud data have demonstrated great potentials in solving
classical problems in 3D computer vision such as 3D object classification and segmentation …

In this paper, we rethink the problem of scene reconstruction from an embodied agent's
perspective: While the classic view focuses on the reconstruction accuracy, our new …

We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene,
enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction …

In this paper, we propose the multi-view saliency guided deep neural network (MVSG-DNN)
for 3D object retrieval and classification. This method mainly consists of three key modules …

2D image based 3D model retrieval is a challenging research topic in the field of 3D model
retrieval. The huge gap between two modalities-2D image and 3D model, extremely …

Most existing 3D object classification and retrieval algorithms rely on one-off supervised
learning on closed 3D object sets and tend to provide rigid convolutional neural networks …

Abstract 3D scan geometry and CAD models often contain complementary information
towards understanding environments, which could be leveraged through establishing a …

Monocular image based 3D object retrieval is a novel and challenging research topic in the
field of 3D object retrieval. Given a RGB image captured in real world, it aims to search for …

We consider the problem of localizing relevant subsets of non-rigid geometric shapes given
only a partial 3D query as the input. Such problems arise in several challenging tasks in 3D …

Recent advances in 3-D sensors and 3-D modeling have led to the availability of massive
amounts of 3-D data. It is too onerous and time consuming to manually label a plentiful of 3 …