5 papers
AdaOcc: Adaptive-Resolution Occupancy Prediction
Chao Chen, Ruoyu Wang, Yuliang Guo +4
Autonomous driving in complex urban scenarios requires 3D perception to be both comprehensive and precise. Traditional 3D perception methods focus on object detection, resulting in…
Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion
Su Sun, Cheng Zhao, Yuliang Guo +4
In this paper, we present a novel indoor 3D reconstruction method with occluded surface completion, given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only f…
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Autonomous Driving
Cheng Zhao, Su Sun, Ruoyu Wang +6
Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but al…
SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction
Yuliang Guo, Abhinav Kumar, Cheng Zhao +3
Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization in a NeRF framework updates the in…
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
Yunhao Ge, Hong-Xing Yu, Cheng Zhao +5
A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets. While augmenting real scenes with virtual objects holds promis…