Monocular depth estimation (MDE) is a fundamental task in computer vision and has drawn
increasing attention. Recently, some methods reformulate it as a classification-regression …

Currently prevalent multi-modal 3D detection methods rely on dense detectors that usually
use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature …

In this paper we introduce the first large-scale video prediction model in the autonomous
driving discipline. To eliminate the restriction of high-cost data collection and empower the …

Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn extensive
attention due to its low cost and high efficiency. Although new algorithms for camera-only 3D …

This paper introduces PatchRefiner, an advanced framework for metric single image depth
estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial for …

Monocular 3D object detection (M3OD) is important for autonomous driving. However,
existing deep learning-based methods easily suffer from performance degradation in real …

Pseudo-Labeling (PL) is a critical approach in semi-supervised 3D object detection (SSOD).
In PL, delicately selected pseudo-labels, generated by the teacher model, are provided for …

Recent advances in 3D object detection leveraging multi-view cameras have demonstrated
their practical and economical value in various challenging vision tasks. However, typical …

Abstract Three‐dimensional (3D) object detection, that is, localizing and classifying all
critical objects in a 3D space, is essential for downstream construction scene analysis tasks …

We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain
adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they …