High-quality 3D reconstruction is an important topic in computer graphics and computer
vision with many applications, such as robotics and augmented reality. The advent of …

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 …

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

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 …

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

We study open-world 3D scene understanding, a family of tasks that require agents to
reason about their 3D environment with an open-set vocabulary and out-of-domain visual …

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 …

We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map
from input 2D images with the objective of enabling 3D grounding, segmentation and …

In this paper, we explore a novel point representation for 3D occupancy prediction from multi-
view images, which is named Occupancy as Set of Points. Existing camera-based methods …