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

Temporal object detection has attracted significant attention, but most
popular detection methods cannot leverage rich temporal information in videos.
Very recently, many algorithms have been developed for video detection task,
yet very few approaches can achieve \emph{real-time online} object detection in
videos. In this paper, based on attention mechanism and convolutional long
short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD)
for real-world detection. Distinct from previous methods, we take aim at
temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a
novel structure including a low-level temporal unit as well as a high-level one
(LH-TU) for multi-scale feature maps. Moreover, we develop a creative temporal
analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal
attention mechanism is specially tailored for background suppression and scale
suppression while a ConvLSTM integrates attention-aware features across time.
An association loss and a multi-step training are designed for temporal
coherence. Besides, an online tubelet analysis (OTA) is exploited for
identification. Our framework is evaluated on ImageNet VID dataset and 2DMOT15
dataset. Extensive comparisons on the detection and tracking capability
validate the superiority of the proposed approach. Consequently, the developed
TSSD-OTA achieves a fast speed and an overall competitive performance in terms
of detection and tracking. Finally, a real-world maneuver is conducted for
underwater object grasping. The source code is publicly available at
this https URL

本论文提出了一种基于注意机制和卷积长短时记忆（ConvLSTM）的时序单次检测器（TSSD），以实现视频中的实时在线目标检测。采用 ConvLSTM 来集成时间金字塔特征层次，并使用一个低级时序单元和一个高级单元来设计新颖的结构（LH-TU）进行多尺度特征图。关注 ConvLSTM（AC-LSTM）的创新时间分析单元被设计为时间上的背景抑制和尺度抑制，并跨时间集成具有关注注意力的特征。此外，针对时间一致性设计了联合损失和多步训练。最后，利用在线管道分析（OTA）进行识别。评估结果证明，该方法在检测和跟踪性能方面具有优势。