Open-vocabulary segmentation methods offer promising capabilities in detecting unseen object categories, but the category must be aware and needs to be provided by a human, either via a text prompt or pre-labeled datasets, thus limiting their scalability. We propose 3D-AVS, a method for Auto-Vocabulary Segmentation of 3D point clouds for which the vocabulary is unknown and auto-generated for each input at runtime, thus eliminating the human in the loop and typically providing a substantially larger vocabulary for richer annotations. 3D-AVS first recognizes semantic entities from image or point cloud data and then segments all points with the automatically generated vocabulary. Our method incorporates both image-based and point-based recognition, enhancing robustness under challenging lighting conditions where geometric information from Li-DAR is especially valuable. Our point-based recognition features a Sparse Masked Attention Pooling (SMAP) module to enrich the diversity of recognized objects. To address the challenges of evaluating unknown vocabularies and avoid annotation biases from label synonyms, hierarchies, or semantic overlaps, we introduce the annotation-free Text-Point Semantic Similarity (TPSS) metric for assessing generated vocabulary quality. Our evaluations on nuScenes and ScanNet200 demonstrate 3D-AVS’s ability to generate semantic classes with accurate point-wise segmentations.

As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an Object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://github.com/MathLee/OLBPNet4PFOS.

With the development of the eye-tracking technique, the fixation becomes an emergent interactive mode in many human-computer interaction study field. For a personal target objects segmentation task, although the fixation can be taken as a novel and more convenient interactive input, it induces a heavy ambiguity problem of the input's indication so that the segmentation quality is severely degraded. In this paper, to address this challenge, we develop an "extraction-to-fusion" strategy based iterative lightweight neural network, whose input is composed by an original image, a fixation map and a position map. Our neural network consists of two main parts: The first extraction part is a concise interlaced structure of standard convolution layers and progressively higher dilated convolution layers to better extract and integrate local and global features of target objects. The second fusion part is a convolutional long short-term memory component to refine the extracted features and store them. Depending on the iteration framework, current extracted features are refined by fusing them with stored features extracted in the previous iterations, which is a feature transmission mechanism in our neural network. Then, current improved segmentation result is generated to further adjust the fixation map and the position map in the next iteration. Thus, the ambiguity problem induced by the fixations can be alleviated. Experiments demonstrate better segmentation performance of our method and effectiveness of each part in our model.