1 code implementation • ICCV 2023 • Sanmin Kim, Youngseok Kim, In-Jae Lee, Dongsuk Kum
To address this limitation, we propose a novel 3D object detection model, P2D (Predict to Detect), that integrates a prediction scheme into a detection framework to explicitly extract and leverage motion features.
1 code implementation • ICCV 2023 • Youngseok Kim, Juyeb Shin, Sanmin Kim, In-Jae Lee, Jun Won Choi, Dongsuk Kum
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation.
Ranked #2 on 3D Multi-Object Tracking on nuscenes Camera-Radar
no code implementations • 29 Oct 2022 • Youngseok Kim, Sanmin Kim, Sangmin Sim, Jun Won Choi, Dongsuk Kum
In this way, our 3D detection network can be supervised by more depth supervision from raw LiDAR points, which does not require any human annotation cost, to estimate accurate depth without explicitly predicting the depth map.
no code implementations • 14 Sep 2022 • Youngseok Kim, Sanmin Kim, Jun Won Choi, Dongsuk Kum
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR.
Ranked #6 on 3D Object Detection on nuscenes Camera-Radar
no code implementations • 17 Jun 2022 • Sanmin Kim, Hyeongseok Jeon, Junwon Choi, Dongsuk Kum
Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory.