In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task.
Recent releases of Large Language Models (LLMs), e. g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts.
The performance of point cloud 3D object detection hinges on effectively representing raw points, grid-based voxels or pillars.
In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space.
In this work, we propose DeepMatcher, a deep Transformer-based network built upon our investigation of local feature matching in detector-free methods.
In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud with a straightforward yet effective multi-task network.
Finally, we propose a sign-based gradient surgery to promote the training of CO-Net, thereby emphasizing the usage of task-shared parameters and guaranteeing that each task can be thoroughly optimized.
Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems.
Real-time and high-performance 3D object detection is of critical importance for autonomous driving.
Ranked #2 on 3D Object Detection on nuScenes LiDAR only
And this provides us a great opportunity to think about how shall these data be organized and exploited.