  In this paper, we introduce a new task: Zero-Shot 3D Reasoning Segmentation
for parts searching and localization for objects, which is a new paradigm to 3D
segmentation that transcends limitations for previous category-specific 3D
semantic segmentation, 3D instance segmentation, and open-vocabulary 3D
segmentation. We design a simple baseline method, Reasoning3D, with the
capability to understand and execute complex commands for (fine-grained)
segmenting specific parts for 3D meshes with contextual awareness and reasoned
answers for interactive segmentation. Specifically, Reasoning3D leverages an
off-the-shelf pre-trained 2D segmentation network, powered by Large Language
Models (LLMs), to interpret user input queries in a zero-shot manner. Previous
research have shown that extensive pre-training endows foundation models with
prior world knowledge, enabling them to comprehend complex commands, a
capability we can harness to "segment anything" in 3D with limited 3D datasets
(source efficient). Experimentation reveals that our approach is generalizable
and can effectively localize and highlight parts of 3D objects (in 3D mesh)
based on implicit textual queries, including these articulated 3d objects and
real-world scanned data. Our method can also generate natural language
explanations corresponding to these 3D models and the decomposition. Moreover,
our training-free approach allows rapid deployment and serves as a viable
universal baseline for future research of part-level 3d (semantic) object
understanding in various fields including robotics, object manipulation, part
assembly, autonomous driving applications, augment reality and virtual reality
(AR/VR), and medical applications. The code, the model weight, the deployment
guide, and the evaluation protocol are: http://tianrun-chen.github.io/Reason3D/
