Cross-Modal Benchmarking for Robotic Perception in Natural Environments
Quick Answer
The WildCross benchmark reveals significant limitations in existing vision models for robotic perception in natural environments, highlighting the need for improved depth estimation techniques.
Quick Take
The WildCross benchmark reveals significant limitations in existing vision models for robotic perception in natural environments, highlighting the need for improved depth estimation techniques. With over 476K RGB frames and synchronized lidar data, this benchmark provides critical insights for enhancing field robotics performance.
Key Points
- WildCross features over 476K RGB frames with semi-dense depth annotations.
- Benchmark focuses on place recognition and metric depth estimation challenges.
- Current models trained on urban data struggle in complex natural environments.
- Expanded analysis emphasizes the need for better depth estimation methods.
- Code repository and dataset available at csiro-robotics.github.io/WildCross.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 11563v1 Announce Type: new Abstract: Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trained on structured, urban environments leading to weaknesses in their perception for field robotics tasks. We showcase the limitations of current models using our recently released WildCross benchmark, a new cross-modal benchmark for place recognition and metric depth estimation in large-scale natural environments.
WildCross comprises over 476K sequential RGB frames with semi-dense depth and surface normal annotations, each aligned with accurate 6DoF pose and synchronized dense lidar submaps. In this work, we provide an expanded analysis of the benchmark results from the recent WildCross benchmark, with particular emphasis on expanded metric depth estimation experiments. Access to the code repository and dataset for this work can be found at https://csiro-robotics. github. io/WildCross.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark. This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.