12 citations · 45 across the 9 of their papers we have counts for
9 papers
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection
Xirui Li, Feng Wang, Naiyan Wang +1
In LiDAR-based 3D detection, history point clouds contain rich temporal information helpful for future prediction. In the same way, history detections should contribute to future d…
UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View
Shengchao Zhou, Weizhou Liu, Chen Hu +2
In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex. Since the multi-modal methods…
Pillar R-CNN for Point Cloud 3D Object Detection
Guangsheng Shi, Ruifeng Li, Chao Ma
The performance of point cloud 3D object detection hinges on effectively representing raw points, grid-based voxels or pillars. Recent two-stage 3D detectors typically take the poi…
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
Jiaming Zhang, Kailun Yang, Hao Shi +7
In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) la…
Depth-Adapted CNNs for RGB-D Semantic Segmentation
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGB-D semantic segmentation has motivated research interest thanks to the accessibility of complementary modalities from the input side. Existing works often adopt a two-str…
PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection
Guangsheng Shi, Ruifeng Li, Chao Ma
Real-time and high-performance 3D object detection is of critical importance for autonomous driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxe…
Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
Jiaming Zhang, Kailun Yang, Chaoxiang Ma +3
Panoramic images with their 360-degree directional view encompass exhaustive information about the surrounding space, providing a rich foundation for scene understanding. To unfold…
Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation
Jiaming Zhang, Chaoxiang Ma, Kailun Yang +3
Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of 360-degree sensors, but modern semantic segmentation approaches rely heavily on annotated training data…
DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange
Chaoxiang Ma, Jiaming Zhang, Kailun Yang +2
Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360-degree sensors, but the vast majority of available semantic segmentation training images are c…