Multi-modal 3D object detection has exhibited significant progress in recent
years. However, most existing methods can hardly scale to long-range scenarios
due to their reliance on dense 3D features, which substantially escalate
computational demands and memory usage. In this paper, we introduce
SparseFusion, a novel multi-modal fusion framework fully built upon sparse 3D
features to facilitate efficient long-range perception. The core of our method
is the Sparse View Transformer module, which selectively lifts regions of
interest in 2D image space into the unified 3D space. The proposed module
introduces sparsity from both semantic and geometric aspects which only fill
grids that foreground objects potentially reside in. Comprehensive experiments
have verified the efficiency and effectiveness of our framework in long-range
3D perception. Remarkably, on the long-range Argoverse2 dataset, SparseFusion
reduces memory footprint and accelerates the inference by about two times
compared to dense detectors. It also achieves state-of-the-art performance with
mAP of 41.2% and CDS of 32.1%. The versatility of SparseFusion is also
validated in the temporal object detection task and 3D lane detection task.
Codes will be released upon acceptance.

SparseFusion 是一种基于稀疏三维特征构建的新型多模态融合框架，能够有效实现远程感知，并在长程 3D 感知任务和时间目标检测任务中取得了最优的性能。

稀疏融合：高效稀疏多模态融合框架用于远程三维感知

SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for  Long-Range 3D Perception

Existing datasets for autonomous driving (AD) often lack diversity and
long-range capabilities, focusing instead on 360{\deg} perception and temporal
reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a
large-scale and diverse multimodal dataset collected over two years in various
European countries, covering an area 9x that of existing datasets. ZOD boasts
the highest range and resolution sensors among comparable datasets, coupled
with detailed keyframe annotations for 2D and 3D objects (up to 245m), road
instance/semantic segmentation, traffic sign recognition, and road
classification. We believe that this unique combination will facilitate
breakthroughs in long-range perception and multi-task learning. The dataset is
composed of Frames, Sequences, and Drives, designed to encompass both data
diversity and support for spatio-temporal learning, sensor fusion,
localization, and mapping. Frames consist of 100k curated camera images with
two seconds of other supporting sensor data, while the 1473 Sequences and 29
Drives include the entire sensor suite for 20 seconds and a few minutes,
respectively. ZOD is the only large-scale AD dataset released under a
permissive license, allowing for both research and commercial use. The dataset
is accompanied by an extensive development kit. Data and more information are
available online (this https URL).

Zenseact Open Dataset (ZOD) 是一种大规模且多样化的自动驾驶数据集，采集了两年时间在欧洲各地的数据，涵盖了比现有数据集多 9 倍的区域，并支持长距离感知和多任务学习。该数据集是通过各种帧、序列和驱动器收集的，为传感器融合、本地化和建图提供了支持。

Zenseact 开放数据集：一种用于自动驾驶的大规模和多样化的多模态数据集

Zenseact Open Dataset: A large-scale and diverse multimodal dataset for  autonomous driving

Autonomous driving is a popular research area within the computer vision
research community. Since autonomous vehicles are highly safety-critical,
ensuring robustness is essential for real-world deployment. While several
public multimodal datasets are accessible, they mainly comprise two sensor
modalities (camera, LiDAR) which are not well suited for adverse weather. In
addition, they lack far-range annotations, making it harder to train neural
networks that are the base of a highway assistant function of an autonomous
vehicle. Therefore, we introduce a multimodal dataset for robust autonomous
driving with long-range perception. The dataset consists of 176 scenes with
synchronized and calibrated LiDAR, camera, and radar sensors covering a
360-degree field of view. The collected data was captured in highway, urban,
and suburban areas during daytime, night, and rain and is annotated with 3D
bounding boxes with consistent identifiers across frames. Furthermore, we
trained unimodal and multimodal baseline models for 3D object detection. Data
are available at https://github.com/aimotive/aimotive_dataset.

该研究介绍了一个多模态数据集，其中包括雷达、相机和激光雷达，以提高自主驾驶汽车的稳健性和长程感知并训练 3D 物体检测模型，并在 GitHub 上提供数据。