BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection (2203.17054v3)

Abstract: Single frame data contains finite information which limits the performance of the existing vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the performance boundary in this area, a novel paradigm dubbed BEVDet4D is proposed to lift the scalable BEVDet paradigm from the spatial-only 3D space to the spatial-temporal 4D space. We upgrade the naive BEVDet framework with a few modifications just for fusing the feature from the previous frame with the corresponding one in the current frame. In this way, with negligible additional computing budget, we enable BEVDet4D to access the temporal cues by querying and comparing the two candidate features. Beyond this, we simplify the task of velocity prediction by removing the factors of ego-motion and time in the learning target. As a result, BEVDet4D with robust generalization performance reduces the velocity error by up to -62.9%. This makes the vision-based methods, for the first time, become comparable with those relied on LiDAR or radar in this aspect. On challenge benchmark nuScenes, we report a new record of 54.5% NDS with the high-performance configuration dubbed BEVDet4D-Base, which surpasses the previous leading method BEVDet-Base by +7.3% NDS. The source code is publicly available for further research at https://github.com/HuangJunJie2017/BEVDet .

• The paper introduces BEVDet4D, which integrates temporal cues into BEV frameworks to advance 3D object detection and improve velocity prediction.
• It employs spatial alignment and frame feature fusion to simplify velocity estimation by predicting positional offsets between frames.
• Experiments on the nuScenes benchmark demonstrate a 7.3% increase in NDS and a 62.9% reduction in velocity error, enhancing autonomous driving safety.

An Expert Overview of "BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection"

The paper "BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection" introduces a novel approach aimed at advancing vision-based multi-camera 3D object detection by incorporating temporal cues, marking a progression from the previously spatial-only methods.

Core Contributions

This research addresses the limitations of traditional single-frame data in vision-based 3D object detection, particularly in velocity prediction. The proposed BEVDet4D paradigm extends the capabilities of the BEVDet framework by integrating temporal information, effectively transforming it from a 3D spatial working space into a 4D spatial-temporal domain. A significant adjustment is made by fusing features from the previous frame with the current frame, thus enabling temporal cue extraction without substantial computational overhead.

Methodological Advancements

The approach simplifies velocity prediction by translating it into positional offset prediction between adjacent frames. This is facilitated by retaining intermediate BEV features and using a spatial alignment process to address variations caused by ego-motion. The temporal fusion in BEVDet4D involves minimal alterations to the existing BEVDet architecture, preserving its scalability and elegance.

Empirical Results

The effectiveness of BEVDet4D is demonstrated on the nuScenes benchmark. It establishes a new performance record with a notable 54.5% NDS when configured as BEVDet4D-Base, outperforming previous methods, including the strong BEVDet-Base, by a 7.3% margin. Particularly noteworthy is the reduction in velocity error by up to 62.9%, bringing vision-based methods to a competitive level with LiDAR and radar systems in terms of velocity prediction, an area they historically lagged in.

Implications and Future Directions

BEVDet4D's results suggest significant practical implications for autonomous driving systems, where enhanced velocity prediction contributes to improved safety and reliability. Theoretically, the integration of temporal cues into vision-based frameworks opens new avenues for future research in autonomous vehicle perception systems.

Future developments could explore optimizing temporal fusion techniques and expanding the use of temporal data in other vision-based tasks such as BEV semantic segmentation and motion prediction. The release of BEVDet4D's source code further encourages exploration and innovation in this domain.

In conclusion, BEVDet4D exemplifies a meaningful step toward harnessing temporal information in multi-camera 3D object detection, offering robust performance gains with minimal computational trade-offs, thus fostering further advancements in AI-powered autonomous driving technologies.