Unitr: A unified and efficient multi-modal transformer for bird's-eye-view representation
Jointly processing information from multiple sensors is crucial to achieving accurate and
robust perception for reliable autonomous driving systems. However, current 3D perception …
robust perception for reliable autonomous driving systems. However, current 3D perception …
Mtr++: Multi-agent motion prediction with symmetric scene modeling and guided intention querying
Motion prediction is crucial for autonomous driving systems to understand complex driving
scenarios and make informed decisions. However, this task is challenging due to the diverse …
scenarios and make informed decisions. However, this task is challenging due to the diverse …
Torchsparse++: Efficient training and inference framework for sparse convolution on gpus
Sparse convolution plays a pivotal role in emerging workloads, including point cloud
processing in AR/VR, autonomous driving, and graph understanding in recommendation …
processing in AR/VR, autonomous driving, and graph understanding in recommendation …
Detecting as labeling: Rethinking lidar-camera fusion in 3d object detection
J Huang, Y Ye, Z Liang, Y Shan, D Du - European Conference on …, 2025 - Springer
Abstract 3D object Detection with LiDAR-camera encounters overfitting in algorithm
development derived from violating some fundamental rules. We refer to the data annotation …
development derived from violating some fundamental rules. We refer to the data annotation …
Fsd v2: Improving fully sparse 3d object detection with virtual voxels
LiDAR-based fully sparse architecture has gained increasing attention. FSDv1 stands out as
a representative work, achieving impressive efficacy and efficiency, albeit with intricate …
a representative work, achieving impressive efficacy and efficiency, albeit with intricate …
Once detected, never lost: Surpassing human performance in offline LiDAR based 3D object detection
This paper aims for high-performance offline LiDAR-based 3D object detection. We first
observe that experienced human annotators annotate objects from a track-centric …
observe that experienced human annotators annotate objects from a track-centric …
Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey.
OA Fawole, DB Rawat - AI, 2024 - search.ebscohost.com
The development of self-driving or autonomous vehicles has led to significant
advancements in 3D object detection technologies, which are critical for the safety and …
advancements in 3D object detection technologies, which are critical for the safety and …
HEDNet: A hierarchical encoder-decoder network for 3d object detection in point clouds
Abstract 3D object detection in point clouds is important for autonomous driving systems. A
primary challenge in 3D object detection stems from the sparse distribution of points within …
primary challenge in 3D object detection stems from the sparse distribution of points within …
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-
label generation and iterative self-training processes. However the challenge arises due to …
label generation and iterative self-training processes. However the challenge arises due to …
Query-based temporal fusion with explicit motion for 3d object detection
Effectively utilizing temporal information to improve 3D detection performance is vital for
autonomous driving vehicles. Existing methods either conduct temporal fusion based on the …
autonomous driving vehicles. Existing methods either conduct temporal fusion based on the …