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Found 7 papers, 1 papers with code
FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection
Multi-sensor modal fusion has demonstrated strong advantages in 3D object detection tasks.
PUPS: Point Cloud Unified Panoptic Segmentation
Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points.
INT: Towards Infinite-frames 3D Detection with An Efficient Framework
By employing INT on CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost with only 2~4ms latency overhead, and currently SOTA on the Waymo 3D Detection leaderboard.
Multimodal trajectory forecasting based on discrete heat map
In Argoverse motion forecasting competition, the task is to predict the probabilistic future trajectory distribution for the interested targets in the traffic scene.
CenterAtt: Fast 2-stage Center Attention Network
In this technical report, we introduce the methods of HIKVISION_LiDAR_Det in the challenge of waymo open dataset real-time 3D detection.
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
The voxel-based view is regular, but sparse, and computation grows cubically when voxel resolution increases.
Ranked #15 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix
Recently, various works that attempted to introduce rotation invariance to point cloud analysis have devised point-pair features, such as angles and distances.
