Pei Sun

TL;DR: This work introduces a new large scale, high quality, diverse dataset, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies, and studies the effects of dataset size and generalization across geographies on 3D detection methods.

TL;DR: In this paper, a large scale, high quality, and diverse dataset for self-driving data is presented, consisting of LiDAR and camera data captured across a range of urban and suburban geographies.

TL;DR: Li et al. as discussed by the authors proposed an end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both birds-eye view and perspective view.

TL;DR: This paper aims to synergize the birds-eye view and the perspective view and proposes a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both and significantly improves detection accuracy over the comparable single-view PointPillars baseline.

TL;DR: RSN as discussed by the authors predicts foreground points from range images and applies sparse convolutions on the selected foreground points to detect objects, which achieves state-of-the-art detection performance on the WOD dataset.