3 citations · 11 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps
Hengyuan Zhang, David Paz, Yuliang Guo +5
Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when ge…
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…
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
Abhinav Kumar, Yuliang Guo, Xinyu Huang +2
Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute th…
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…