Large-scale point cloud semantic segmentation is a challenging task due to the complexity and diversity of real-world 3D scenes. Most existing methods primarily rely on spatial coordinates to learn geometric representations without fully exploring local structural relationships. Additionally, the semantic gap between the encoder and decoder in segmentation networks is an important factor that constrains model performance. To address these challenges, we propose a novel network architecture called GAF-Net, which comprises a Geometric Contextual Feature Aggregation (GCFA) module and a Multi-scale Feature Adaptive Fusion (MFAF) module. The GCFA module consists of three primary blocks: (1) a Geometric Edge Representation block, designed to leverage spatial relative position and orientation information between the center point and its neighbors to capture detailed local geometric structural relations; (2) a Point Geometry Prior block, aimed at extracting explicit geometric priors for each point from raw point clouds. This block is lightweight and parameter-free; (3) a Geometry-Aware Attentive Pooling block, which combines semantic features with learned geometric representations, enabling the learning and aggregation of informative local contextual features. Our proposed MFAF module integrates multi-scale features by introducing an adaptive fusion approach. It effectively bridges the semantic gap between the encoder and decoder and mitigates the information loss caused by random sampling. Extensive experimental results on three large-scale benchmark datasets including S3DIS, Toronto3D, and SemanticKITTI demonstrate the superior performance of our proposed GAF-Net. 

GAF-Net: Geometric Contextual Feature Aggregation and Adaptive Fusion for Large-Scale Point Cloud Semantic Segmentation

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s ability to fully comprehend 3D shapes and adversely impacts the text-image fusion in a shared latent space. To this end, we propose a novel approach called Differentiable rendering-based multi-view Image-Language Fusion (DILF) for zero-shot 3D shape understanding. Specifically, DILF leverages large-scale language models (LLMs) to generate textual prompts enriched with 3D semantics and designs a differentiable renderer with learnable rendering parameters to produce representative multi-view images. These rendering parameters can be iteratively updated using a text-image fusion loss, which aids in parameters’ regression, allowing the model to determine the optimal viewpoint positions for each 3D object. Then a group-view mechanism is introduced to model interdependencies across views, enabling efficient information fusion to achieve a more comprehensive 3D shape understanding. Experimental results can demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification. The code is available at https://github.com/yuzaiyang123/DILP. 

DILF: Differentiable rendering-based multi-view Image-Language Fusion for zero-shot 3D shape understanding

LiDAR and camera are two common vision sensors used in the real world, producing complementary point cloud and image data. While multimodal data has previously been found mostly in 3D detection and tracking, we aim to study large-scale semantic segmentation by multimodal data fusion rather than only knowledge transfer or distillation. We show that fusing LiDAR features with camera features and abandoning the strict point-to-pixel hard correlation can lead to better performance. Even so, it is still difficult to make full use of multimodal data due to the spatiotemporal misalignment of sensors and uneven data distribution.. To address this issue, we propose the Joint Semantic Segmentation (JoSS), a powerful LiDAR-camera fusion solution that employs the attention mechanism to explore the potential relationships between point clouds and images. Specifically, JoSS consists of commonly used 3D and 2D backbones, and lightweight transformer decoders based on point clouds and images. A point cloud decoder adopts queries to analyze the semantics from LiDAR features, and an image decoder adaptively fuses these queries with corresponding image features. Both exploit contextual information, thus fully mining multimodal information for semantic segmentation. In addition, we propose an effective unimodal data augmentation (UDA) method that performs cross-modal contrastive learning on point clouds and images to significantly improve accuracy by augmenting the point cloud alone without the complexity of generating paired samples of both modalities. Our Joss achieves advanced results in two widely used large-scale benchmarks, i.e. SemanticKITTI and nuScenes-lidarseg. 

Joint Semantic Segmentation using representations of LiDAR point clouds and camera images

Existing work shows that 3D point clouds produce only about a 4% drop in semantic segmentation even at 1% random point annotation, which inspires us to further explore how to achieve better results at lower cost. As scene point clouds provide position and color information and often used in tandem as the only input, with little work going into segmentation by fusing information from dual spaces. To optimize point cloud representations, we propose a novel framework for the dual representation query network (DRQNet). The proposed framework partitions the input point cloud into position and color spaces, using the separately extracted geometric structure and semantic context to create an internal supervisory mechanism that bridges the dual spaces and fuses the information. Adopting sparsely annotated points as the query set, DRQNet provide guidance and perceptual information for multi-stage point clouds through random sampling. More, to differentiate and enhance the features generated by local neighbourhoods within multiple perceptual fields, we design a representation selection module to identify the contributions made by the position and color of each query point, and weight them adaptively according to reliability. The proposed DRQNet is robust to point cloud analysis and eliminates the effects of irregularities and disorder. Our method achieves significant performance gains on three mainstream benchmarks.

Exploring Dual Representations in Large-Scale Point Clouds: A Simple Weakly Supervised Semantic Segmentation Framework

Context-based local-global fusion network for 3D point cloud classification and segmentation

PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis

Small object detection is a critical challenge in Unmanned Aerial Vehicles (UAVs) due to the limited pixel representation of small objects and the impact of successive pooling operations, which frequently results in the disappearance of small objects within intricate backgrounds. To tackle this issue, we propose the Small Object Enhancement Pyramid (SOEP) module, which first transforms feature representations (i.e., in the spatial domain) into the frequency domain to better capture small objects typically characterized by high-frequency components. These feature representations are then fused in the spatial domain using a frequency-based attention map, enhancing small object representations by integrating information from both complementary domains. Furthermore, we introduce a Task Aligned Head (TAH) that integrates classification and localization tasks interactively, reducing the misalignment that occurs when these tasks are learned independently, particularly in the context of small objects. Experimental results on the Visdrone dataset verify that our proposed method (D2FTA) outperforms the baseline method by 12.7%, 14.19% on mAP0.5 and mAP0.5:0.95.

Dual-Domain Feature-Guided Task Alignment for Enhanced Small Object Detection

Revisiting a Simple MLP Framework for Z-Axis Rotation-Invariant Point Cloud Place Recognition

Topological Fusion Model for Molecular Property Prediction

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