Abstract Three-dimensional (3D) human pose estimation involves estimating the articulated
3D joint locations of a human body from an image or video. Due to its widespread …

Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This
appealing ability is vital for recognition and understanding. To enable such capability in AI …

We propose pixelNeRF, a learning framework that predicts a continuous neural scene
representation conditioned on one or few input images. The existing approach for …

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense
geometry and semantics of a scene are inferred from a single monocular RGB image …

Richly segmented 3D scene reconstructions are an integral basis for many high-level scene
understanding tasks, such as for robotics, motion planning, or augmented reality. Existing …

Semantic scene completion (SSC) aims to jointly estimate the complete geometry and
semantics of a scene, assuming partial sparse input. In the last years following the …

Recent advances in neural reconstruction enable high-quality 3D object reconstruction from
casually captured image collections. Current techniques mostly analyze their progress on …

Abstract 3D reconstruction from a single 2D image was extensively covered in the literature
but relies on depth supervision at training time, which limits its applicability. To relax the …

Holistic 3D scene understanding entails estimation of both layout configuration and object
geometry in a 3D environment. Recent works have shown advances in 3D scene estimation …

We present the first single-view 3D reconstruction network aimed at recovering geometric
details from an input image which encompass both topological shape structures and surface …