2 citations · 3 across the 7 of their papers we have counts for
8 papers
Synthetic-to-Real Translation for Class-Agnostic Motion Prediction
Yizheng Wu, Hongwei Fan, Kewei Wang +8
Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A key challenge in this domain…
Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Ruibo Li, Hanyu Shi, Zhe Wang +1
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakl…
DSOcc: Leveraging Depth Awareness and Semantic Aid to Boost Camera-Based 3D Semantic Occupancy Prediction
Naiyu Fang, Zheyuan Zhou, Kang Wang +5
Camera-based 3D semantic occupancy prediction offers an efficient and cost-effective solution for perceiving surrounding scenes in autonomous driving. However, existing works rely…
TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
Yiran Wang, Jiaqi Li, Chaoyi Hong +6
Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-ti…
iControl3D: An Interactive System for Controllable 3D Scene Generation
Xingyi Li, Yizheng Wu, Jun Cen +6
3D content creation has long been a complex and time-consuming process, often requiring specialized skills and resources. While recent advancements have allowed for text-guided 3D…
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
Xingyi Li, Zhiguo Cao, Yizheng Wu +4
Current 3D stylization methods often assume static scenes, which violates the dynamic nature of our real world. To address this limitation, we present S-DyRF, a reference-based spa…
Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations
Kewei Wang, Yizheng Wu, Jun Cen +5
The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems, wherein class-agnostic motion prediction methods directly pr…
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
Kewei Wang, Yizheng Wu, Zhiyu Pan +5
Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-perform…