2 citations · 2 across the 6 of their papers we have counts for
6 papers
Early High-Frequency Injection for Geometry-Sensitive OOD Detection
Chuanjie Cheng, Ningkang Peng, Chenxi Liu +3
Post-hoc OOD detectors score logits or features after training, so their success depends on the geometry already encoded in the representation. We revisit this assumption through a…
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection
Tong He, Pei Sun, Zhaoqi Leng +3
We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds. Our main motivation is fusing object-aware latent embeddings i…
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
Yingwei Li, Charles R. Qi, Yin Zhou +2
Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded…
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Zhaoqi Leng, Guowang Li, Chenxi Liu +5
Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly…
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
Pei Sun, Mingxing Tan, Weiyue Wang +4
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse na…
Multi-Class 3D Object Detection with Single-Class Supervision
Mao Ye, Chenxi Liu, Maoqing Yao +4
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost. An alternative approach is to…