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Found 5 papers, 2 papers with code
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29. 0% to 57. 4%.
Ranked #21 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset.
Lidar Range Image Compression with Deep Delta Encoding
However, most prior work focus on the generic point cloud representation, neglecting the spatial patterns of the points from lidar range images.
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding
Lidars are depth measuring sensors widely used in autonomous driving and augmented reality.
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task.
