Dogun Kim

LMDrive: Closed-Loop End-to-End Driving with Large Language Modelshttps://arxiv.org/abs/2312.07488 LMDrive: Closed-Loop End-to-End Driving with Large Language ModelsDespite significant recent progress in the field of autonomous driving, modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events and challenging urban scenarios. On the one hand, lar..

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bitshttps://arxiv.org/abs/2402.17764 The Era of 1-bit LLMs: All Large Language Models are in 1.58 BitsRecent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is tern..

https://arxiv.org/abs/2203.08195 DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object DetectionLidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods simply decorate raw lidar point clouds with camera features and feed them directly to existing 3D detarxiv.org - AbstractLiDAR와 카메라는 3D de..

2. PointNet ++ https://arxiv.org/abs/1706.02413 PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpaceFew prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grainearxiv.orgCNN은 이미..

딥러닝을 이용해 Point cloud를 처리하는 방법론들에 대해 공부 1. PointNethttps://arxiv.org/abs/1612.00593 PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationPoint cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and ca..