In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

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 …

In this research, we propose a new 3D object detector with a trustworthy depth estimation,
dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …

Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

We present cross-view transformers, an efficient attention-based model for map-view
semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping …

This paper tackles the problem of depth estimation from a single image. Existing work either
focuses on generalization performance disregarding metric scale, ie relative depth …

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall
into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is …

Abstract Training a Neural Radiance Field (NeRF) without pre-computed camera poses is
challenging. Recent advances in this direction demonstrate the possibility of jointly …

Understanding and reasoning about spatial relationships is crucial for Visual Question
Answering (VQA) and robotics. Vision Language Models (VLMs) have shown impressive …

Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth
from a single image is geometrically ill-posed and requires scene understanding so it is not …