Search Results for author:
Found 12 papers, 5 papers with code
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
Instead of directly training a depth prediction network, we unify the image and LiDAR features in the Bird-Eye-View (BEV) space and adaptively transfer knowledge across non-homogenous representations in a teacher-student paradigm.
Ranked #14 on
3D Object Detection
on nuScenes Camera Only
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
Recently, AutoAlign presents a learnable paradigm in combining these two modalities for 3D object detection.
Towards Model Generalization for Monocular 3D Object Detection
However, caused by severe domain gaps (e. g., the field of view (FOV), pixel size, and object size among datasets), Mono3D detectors have difficulty in generalization, leading to drastic performance degradation on unseen domains.
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Based on this, we develop a teacher-student paradigm to generate adaptive pseudo labels on the target domain.
Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding.
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner.
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations
To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks.
Multimodal Motion Prediction with Stacked Transformers
Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving.
TPNet: Trajectory Proposal Network for Motion Prediction
However, it remains difficult for these methods to provide multimodal predictions as well as integrate physical constraints such as traffic rules and movable areas.
EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection
EdgeStereo also achieves comparable generalization performance for disparity estimation because of the incorporation of edge cues.
Discriminative Representation Combinations for Accurate Face Spoofing Detection
Secondly we utilize the SSD, which is a deep learning framework for detection, to excavate context cues and conduct end-to-end face presentation attack detection.
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching
Recent convolutional neural networks, especially end-to-end disparity estimation models, achieve remarkable performance on stereo matching task.
