SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition (2106.11481v1)

Abstract: Place Recognition is a crucial capability for mobile robot localization and navigation. Image-based or Visual Place Recognition (VPR) is a challenging problem as scene appearance and camera viewpoint can change significantly when places are revisited. Recent VPR methods based on sequential representations'' have shown promising results as compared to traditional sequence score aggregation or single image based techniques. In parallel to these endeavors, 3D point clouds based place recognition is also being explored following the advances in deep learning based point cloud processing. However, a key question remains: is an explicit 3D structure based place representation always superior to an implicitspatial'' representation based on sequence of RGB images which can inherently learn scene structure. In this extended abstract, we attempt to compare these two types of methods by considering a similar ``metric span'' to represent places. We compare a 3D point cloud based method (PointNetVLAD) with image sequence based methods (SeqNet and others) and showcase that image sequence based techniques approach, and can even surpass, the performance achieved by point cloud based methods for a given metric span. These performance variations can be attributed to differences in data richness of input sensors as well as data accumulation strategies for a mobile robot. While a perfect apple-to-apple comparison may not be feasible for these two different modalities, the presented comparison takes a step in the direction of answering deeper questions regarding spatial representations, relevant to several applications like Autonomous Driving and Augmented/Virtual Reality. Source code available publicly https://github.com/oravus/seqNet.

• The paper demonstrates that image sequence-based methods can rival and sometimes outperform traditional 3D point cloud approaches for visual place recognition.
• It employs the Oxford Robotcar dataset and Recall@K metrics to reveal the benefits of temporal descriptors over sparser 3D data.
• The study highlights the importance of non-overlapping training splits and suggests future integration of both modalities for enhanced VPR.

SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition

The paper investigates the comparative efficacy of two modalities—image sequence-based and 3D point cloud-based approaches—for Visual Place Recognition (VPR) amidst challenging conditions like day-night variations. The authors assess SeqNetVLAD, a sequential descriptor image-based approach, against PointNetVLAD, a well-regarded point cloud-based method. The primary aim is to evaluate whether explicit 3D structure representations invariably outperform implicit image sequence-based spatial representations.

Core Contributions

1. Problem Context: Mobile robot localization and navigation rely heavily on VPR, where scene recognition is susceptible to visual changes due to varying appearances and viewpoints over time. Image-based and 3D point cloud-based methods are both crucial in tackling these challenges.
2. Sequential Descriptors: Recent advancements in sequential descriptors, such as SeqNetVLAD, have shown promise by utilizing the inherent temporal continuity and structural consistency in image sequences.
3. Comparative Analysis: By focusing on a comparable metric span, the authors critically analyze the performance of sequential image descriptors versus point cloud descriptors, showcasing that image sequence-based methods can rival or exceed traditional methods for certain conditions.

Experimental Design and Results

The research employs the Oxford Robotcar dataset, conducting experiments with SeqNet and PointNetVLAD under identical conditions. The key performance metric was Recall@K, a common benchmark in VPR evaluation. The results reveal that while sequence-based methods like SeqNetVLAD possess the potential to match and, in certain cases, surpass point cloud methods like PointNetVLAD for VPR, they do so by implicitly understanding the 3D structure through sequence information rather than explicit 3D modeling.

1. Performance Metrics: SeqNetVLAD achieved superior recall rates across various $K$ values compared to PointNetVLAD, highlighting the potential of temporal descriptors in overcoming appearance-induced challenges.
2. Data Accumulation Strategy: The research highlights that the sequence-based approach utilizes richer RGB sensor data through an advantageous accumulation strategy, leading to an augmentation of performance capabilities compared to the sparse data density of point clouds.
3. Training Splits: The authors also emphasize the importance of ensuring non-overlapping training and testing splits to prevent data leakage and improve the robustness of the experimental findings.

Implications and Future Directions

This paper underscores the potential advantages of leveraging image sequences for VPR, particularly in environments where visual conditions are highly variable. The complementary nature of image sequences and 3D data suggests a tantalizing possibility for integrated systems that harness the advantages of both modalities. Future research directions outlined include the possibility of fusing 2D image sequences with 3D point clouds, potentially yielding a hybrid representation that marries temporal coherence with spatial accuracy.

Furthermore, understanding the intrinsic merits and constraints of each approach could inform more robust VPR systems in sectors like autonomous driving and augmented/virtual reality. As these domains grow increasingly dependent on sophisticated environmental perception, evolving spatial representations that can seamlessly adapt to environmental variations become imperative.

The paper provides a critical comparative lens, paving the way for further investigation into how best to integrate and optimize these distinct methodological streams for superior spatial understanding in artificial intelligence applications.