8 papers with code • 1 benchmarks • 3 datasets
Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding.
Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction.
Ranked #2 on Room Layout Estimation on SUN RGB-D (using extra training data)
We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model.
Ranked #4 on Monocular 3D Object Detection on SUN RGB-D
Holistic 3D indoor scene understanding refers to jointly recovering the i) object bounding boxes, ii) room layout, and iii) camera pose, all in 3D.
Ranked #3 on Monocular 3D Object Detection on SUN RGB-D
To address this problem, we propose ImVoxelNet, a novel fully convolutional method of 3D object detection based on monocular or multi-view RGB images.
Ranked #1 on Monocular 3D Object Detection on SUN RGB-D
Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space.
Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image.