Abstract 3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric …

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 …

Abstract Monocular 3D Semantic Scene Completion (SSC) has garnered significant
attention in recent years due to its potential to predict complex semantics and geometry …

Semantic occupancy perception is essential for autonomous driving, as automated vehicles
require a fine-grained perception of the 3D urban structures. However, existing relevant …

Abstract 3D occupancy prediction is an important task for the robustness of vision-centric
autonomous driving which aims to predict whether each point is occupied in the surrounding …

Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

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 …

Training deep models for semantic scene completion is challenging due to the sparse and
incomplete input, a large quantity of objects of diverse scales as well as the inherent label …

Vision-language navigation (VLN) requires an agent to navigate through an 3D environment
based on visual observations and natural language instructions. It is clear that the pivotal …

Semantic scene understanding is important for various applications. In particular, self-driving
cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light …