Search Results for author:
Found 7 papers, 4 papers with code
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision
In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision.
DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation
Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage.
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation.
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation
In this paper, we propose LoopDA for domain adaptive nighttime semantic segmentation.
Monocular Depth Estimation by Learning from Heterogeneous Datasets
Depth estimation provides essential information to perform autonomous driving and driver assistance.
Ranked #43 on
Monocular Depth Estimation
on KITTI Eigen split
Multimodal End-to-End Autonomous Driving
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
On the Metrics for Evaluating Monocular Depth Estimation
In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception.
