Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection (2210.02443v1)

Abstract: While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception. Observing that existing works' fusion of multi-frame images are instances of temporal stereo matching, we find that performance is hindered by the interplay between 1) the low granularity of matching resolution and 2) the sub-optimal multi-view setup produced by limited history usage. Our theoretical and empirical analysis demonstrates that the optimal temporal difference between views varies significantly for different pixels and depths, making it necessary to fuse many timesteps over long-term history. Building on our investigation, we propose to generate a cost volume from a long history of image observations, compensating for the coarse but efficient matching resolution with a more optimal multi-view matching setup. Further, we augment the per-frame monocular depth predictions used for long-term, coarse matching with short-term, fine-grained matching and find that long and short term temporal fusion are highly complementary. While maintaining high efficiency, our framework sets new state-of-the-art on nuScenes, achieving first place on the test set and outperforming previous best art by 5.2% mAP and 3.7% NDS on the validation set. Code will be released $\href{https://github.com/Divadi/SOLOFusion}{here.}$

• The paper demonstrates that integrating extensive temporal data via SOLOFusion addresses the limitations of short frame histories in camera-only 3D detection.
• It introduces a unified stereo formulation that combines high-resolution short-term and low-resolution long-term fusion for more accurate depth estimation.
• The framework achieved a 5.2% mAP and 3.7% NDS boost on the nuScenes dataset, underscoring its practical impact on 3D object detection.

Analysis of "Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection"

The paper, "Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection," explores the domain of camera-only 3D detection by proposing a novel approach leveraging temporal multi-view stereo matching. The research critically examines how current models utilize short temporal histories, which restrict the success of multi-frame integration for object detection, and offers innovative solutions to mitigate the resulting limitations. The authors introduce SOLOFusion, a new framework that skillfully uses both long-term and short-term temporal data constructs to bridge critical gaps in localization potential.

The proposed paper's contribution is multifaceted:

• Unified Stereo Formulation: The paper suggests that temporal camera-based 3D detection frameworks operate as forms of multi-view stereo matching. Under this formulation, it postulates that the short time windows and low feature resolutions traditionally used are the chief constraints. Temporal fusion and feature resolution are emphasized as pivotal to the quality of depth estimation, and the paper tailors their framework, SOLOFusion, accordingly.
• Localization Potential: The crux of the paper's approach is the notion of localization potential, defined through the lens of multi-view depth estimation ease, which makes the depth matching task clearer. The formulation illustrates that optimal temporal separation between views is non-linear across depth and pixel variations. Consequently, there is no universal temporal difference beneficial to all scenarios, prompting the authors to advocate for utilizing extensive temporal history.
• Complementary Temporal Fusion Strategies: SOLOFusion promotes the dual use of high-resolution short-term and low-resolution long-term temporal fusion. This strategy starkly contrasts prior methods focusing on fewer frames and aligns with the findings that long-term past aggregation can compensate for feature resolution inadequacies. Simultaneously, this framework leverages high-resolution features for fine-grained depth prediction, particularly by employing a Gaussian-Spaced Top-k sampling method for depth hypotheses, balancing exploitation of predicted monocular depths with exploration of possible alternatives.

The results presented validate the reformulation's promise. Notably, SOLOFusion exhibited superior performance metrics—improving mAP by 5.2% and NDS by 3.7%—over the current best on the nuScenes dataset. This improvement underscores the synergy between assessing temporal scope and image resolution—a crucial consideration demonstrated by the framework’s ability to effectively balance runtime efficiency against resource demands.

In conclusion, this research not only provides insights into the nuances of temporal multi-view 3D object detection but also establishes a more effective baseline for future work. The analyses underscore the need for adaptive and extensive temporal integration over traditional depth estimation constraints. Future research might investigate further into dynamic balancing of time and resolution, augmenting theoretical underpinnings with practical frameworks such as SOLOFusion, to continue advancing the precision and utility of camera-only 3D detection systems. Continued exploration of deep learning methodologies in processing temporal data may propel autonomous vehicle perception capabilities further.