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PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion


Core Concepts
The author introduces PointSSC, a benchmark for semantic scene completion using cooperative vehicle and infrastructure views. They propose a LiDAR-based model with innovative features to advance semantic point cloud completion.
Abstract
PointSSC is introduced as the first cooperative vehicle-infrastructure point cloud benchmark for semantic scene completion. It addresses the limitations of existing SSC models by leveraging point clouds for efficient representation. The dataset provides long-range perception scenes with minimal occlusion, driving advances in real-world navigation. The proposed model includes a Spatial-Aware Transformer and Completion and Segmentation Cooperative Module for joint completion and segmentation tasks. The content discusses the importance of accurate 3D scene perception for autonomous agents, highlighting the significance of holistic understanding in path planning and collision avoidance. Existing SSC datasets are critiqued for their limited perception range and susceptibility to occlusion compared to infrastructure sensors. The introduction of PointSSC aims to bridge this gap by utilizing both vehicle-side and infrastructure-side perspectives. The generation pipeline of PointSSC involves automated annotation leveraging Semantic Segment Anything, enabling efficient assignment of semantics to scenes. Dynamic objects mutual completion is addressed through multi-object multi-view strategies to handle occlusion in point clouds effectively. The proposed LiDAR-based model sets new benchmarks on PointSSC for both completion and segmentation tasks. Experimental results demonstrate the superiority of the proposed model over existing approaches on the PointSSC dataset. Evaluation metrics such as Chamfer Distance (CD), F1-score, and mean class IoU (mIoU) validate the effectiveness of the Spatial-Aware Transformer and Completion and Segmentation Cooperative Module.
Stats
"PointSSC has the largest data volume among existing datasets." "PointSSC covers spatial ranges up to 250m x 140m x 17m." "Our method sets a new state-of-the-art on PointSSC."
Quotes
"We develop PointSSC, the first large-scale outdoor point cloud SSC dataset from cooperative vehicle-infrastructure views." "Our method sets the new state-of-the-art on PointSSC for both completion and semantic segmentation tasks."

Key Insights Distilled From

by Yuxiang Yan,... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2309.12708.pdf
PointSSC

Deeper Inquiries

How can infrastructure-side datasets enhance semantic scene understanding beyond autonomous driving

Infrastructure-side datasets can enhance semantic scene understanding beyond autonomous driving by providing a complementary perspective to vehicle-mounted sensors. These datasets offer a fixed vantage point that captures scenes from different angles and distances, enabling a more comprehensive view of the environment. This additional data can improve the accuracy of semantic annotations, especially in scenarios where vehicles may have blind spots or limited visibility. By combining information from infrastructure sensors with vehicle-mounted sensors, SSC models can benefit from a richer and more detailed representation of the surroundings.

What counterarguments exist against relying solely on vehicle-mounted sensors for SSC models

Relying solely on vehicle-mounted sensors for SSC models presents several limitations that infrastructure-side datasets can help overcome: Limited Perception Range: Vehicle-mounted sensors have restricted perception ranges compared to infrastructure-based sensors, leading to incomplete scene understanding. Occlusion Issues: Vehicles often encounter occlusions due to other objects or structures, hindering accurate perception. Infrastructure-side data can provide unobstructed views for better scene completion. Lack of Long-Range Information: Infrastructure-based datasets offer long-range perception capabilities that are crucial for anticipating events further ahead on the road. Reduced Data Redundancy: Having redundant data sources enhances reliability in case one sensor fails or provides inaccurate information.

How might advancements in semantic scene completion impact other fields beyond navigation

Advancements in semantic scene completion driven by technologies like PointSSC could have far-reaching implications beyond navigation: Robotics: Improved 3D scene understanding is vital for robots operating in dynamic environments where they need to navigate safely and interact with objects intelligently. Augmented Reality (AR) & Virtual Reality (VR): Enhanced semantic scene completion can lead to more realistic AR/VR experiences by creating immersive virtual environments with accurate object interactions. Urban Planning: Detailed semantic mapping enabled by advanced SSC models could aid urban planners in designing safer and more efficient city layouts based on real-world spatial data. Disaster Response: In emergency situations, precise 3D reconstructions through semantic completion could assist first responders in assessing affected areas quickly and planning rescue operations effectively. Environmental Monitoring: Semantic scene completion techniques could be applied to analyze environmental changes over time, such as deforestation patterns or urban expansion impacts on ecosystems. These advancements underscore the broad applicability of improved semantic scene understanding across various domains beyond traditional navigation contexts.
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