Search Results for author:
Found 9 papers, 2 papers with code
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Autonomous driving progress relies on large-scale annotated datasets.
PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection
Pillar-based methods mainly employ randomly initialized 2D convolution neural network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone scaling and pretraining in the image domain.
ADriver-I: A General World Model for Autonomous Driving
Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I.
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection
In light of the success of sample mining techniques in 2D object detection, we propose a simple yet effective mining strategy for improving depth perception in 3D object detection.
Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception
In this paper, we explore an embarrassingly simple long-term recurrent fusion strategy built upon the LSS-based methods and find it already able to enjoy the merits from both sides, i. e., rich long-term information and efficient fusion pipeline.
Towards 3D Object Detection with 2D Supervision
We propose a temporal 2D transformation to bridge the 3D predictions with temporal 2D labels.
PersDet: Monocular 3D Detection in Perspective Bird's-Eye-View
To address this problem, we revisit the generation of BEV representation and propose detecting objects in perspective BEV -- a new BEV representation that does not require feature sampling.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters.
