Search Results for author:
Found 12 papers, 6 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.
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.
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Due to the lack of insight in industrial application, existing methods on open datasets neglect the camera pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters.
Ranked #9 on
Monocular 3D Object Detection
on KITTI Cars Moderate
(using extra training data)
Multimodal Motion Prediction with Stacked Transformers
Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving.
Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction
Existing work addressed this challenge by either learning social spatial interactions represented by the positions of a group of pedestrians, while ignoring their temporal coherence (\textit{i. e.} dependencies between different long trajectories), or by understanding the complicated scene layout (\textit{e. g.} scene segmentation) to ensure safe navigation.
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.
Recursive Social Behavior Graph for Trajectory Prediction
Social interaction is an important topic in human trajectory prediction to generate plausible paths.
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images.
3D Object Detection From Stereo Images
Disparity Estimation
+2
