ORE-Mamba: Objective-Driven Representation Evolution with Mamba for Point Cloud Analysis

Point clouds are a fundamental sensing modality for object perception, scene understanding, and robotic interaction in modern three-dimensional sensing systems. Effective point-cloud understanding requires representations that preserve intrinsic geometric structure while progressively emphasizing task-discriminative information. However, existing Point Mamba architectures typically encode these heterogeneous requirements in a unified token state that evolves along a shared state-space trajectory, potentially limiting functional specialization and complementarity. To address this limitation, we propose ORE-Mamba, an objective-driven representation evolution framework that organizes feature transformations according to their intended functional roles. Here, objective-driven refers to the functional purpose assigned to a representation pathway rather than to an additional training objective or auxiliary loss. ORE-Mamba maintains geometry-oriented and saliency-oriented representations in two distinct but collaborative streams. The geometry stream employs Local Geometry Aggregation Mamba (LGAM) to encode Euclidean neighborhood relations and propagate geometry-aware information over long ranges through a projected-channel bidirectional state-space model (Bi-SSM). The saliency stream employs Receptive-Field Coordinate Attention (RFCA), which combines Morton Z-order serialization with receptive-field-aware position–channel recalibration to selectively enhance task-relevant responses. Stage-wise bidirectional residual interaction exchanges complementary information between the two streams, while token-wise gated fusion adaptively integrates their final representations. Experiments on ModelNet40, ScanObjectNN, and ShapeNetPart demonstrate strong performance in object classification, part segmentation, and few-shot recognition. Without pre-training, ORE-Mamba achieves mean accuracies of 94.0% on ModelNet40 and 95.24%, 94.03%, and 92.11% on the OBJ-BG, OBJ-ONLY, and PB-T50-RS variants of ScanObjectNN, respectively, across three independent training runs. With 15.7 M parameters and 4.7 G FLOPs, ORE-Mamba provides a favorable trade-off between representational capacity and computational cost. These results support objective-driven representation evolution as an effective alternative to unified feature propagation for point-cloud learning.