AI RESEARCH
B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding
arXiv CS.CV
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ArXi:2508.05269v2 Announce Type: replace Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds provide precise spatial geometry and rich temporal cues, making them ideal for representing real-world scenes. However, despite their potential, 4D LiDAR remains underexplored in the context of Multimodal Large Language Models (MLLMs) due to the absence of high-quality, modality-specific annotations and the lack of MLLM architectures capable of processing its high-dimensional composition.