Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

In this work, we present a novel framework built to simplify 3D asset generation for amateur
users. To enable interactive generation, our method supports a variety of input modalities …

We present PanopticNeRF, an object-aware neural scene representation that decomposes
a scene into a set of objects (things) and background (stuff). Each object is represented by a …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

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 …

Recent progress in 3D object detection from single images leverages monocular depth
estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors …

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a
solution with simple configuration compared to typical multi-sensor systems. The main …

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …

Modelling interactions between humans and objects in natural environments is central to
many applications including gaming, virtual and mixed reality, as well as human behavior …