Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Occupancy prediction reconstructs 3D structures of surrounding environments. It provides detailed information for autonomous driving planning and navigation. However, most existing methods heavily rely on the LiDAR point clouds to generate occupancy ground truth, which is not available in the vision-based system. In this paper, we propose an OccNeRF method for training occupancy networks without 3D ground truth. Different from previous works which consider a bounded scene, we parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. The neural rendering is adopted to convert occupancy fields to multi-camera depth maps, supervised by multi-frame photometric consistency. Moreover, for semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model. Extensive experiments for both self-supervised depth estimation and 3D occupancy prediction tasks on nuScenes and SemanticKITTI datasets demonstrate the effectiveness of our method. The code is available at https://github.com/LinShan-Bin/OccNeRF.

Metal dental implants may introduce metal artifacts (MA) during the CBCT imaging process, causing significant interference in subsequent diagnosis. In recent years, many deep learning methods for metal artifact reduction (MAR) have been proposed. Due to the huge difference between synthetic and clinical MA, supervised learning MAR methods may perform poorly in clinical settings. Many existing unsupervised MAR methods trained on clinical data often suffer from incorrect dental morphology. To alleviate the above problems, in this paper, we propose a new MAR method of Coupled Diffusion Models (CDM) for clinical dental CBCT images. Specifically, we separately train two diffusion models on clinical MA-degraded images and clinical clean images to obtain prior information, respectively. During the denoising process, the variances of noise levels are calculated from MA images and the prior of diffusion models. Then we develop a noise transformation module between the two diffusion models to transform the MA noise image into a new initial value for the denoising process. Our designs effectively exploit the inherent transformation between the misaligned MA-degraded images and clean images. Additionally, we introduce an MA-adaptive inference technique to better accommodate the MA degradation in different areas of an MA-degraded image. Experiments on our clinical dataset demonstrate that our CDM outperforms the comparison methods on both objective metrics and visual quality, especially for severe MA degradation. We will publicly release our code.

Nowadays, leveraging 2D images and pre-trained mod- els to guide 3D point cloud feature representation has shown a remarkable potential to boost the performance of 3D fundamental models. While some works rely on additional data such as 2D real-world images and their corre- sponding camera poses, recent studies target at using point cloud exclusively by designing 3D-to-2D projection. How- ever, in the indoor scene scenario, existing 3D-to-2D pro- jection strategies suffer from severe occlusions and incoher- ence, which fail to contain sufficient information for fine- grained point cloud segmentation task. In this paper, we ar- gue that the crux of the matter resides in the basic premise of existing projection strategies that the medium is homo- geneous, thereby projection rays propagate along straight lines and behind objects are occluded by front ones. In- spired by the phenomenon of mirage where the occluded objects are exposed by distorted light rays due to heteroge- neous medium refraction rate, we propose MirageRoom by designing parametric mirage projection with heterogeneous medium to obtain series of projected images with various distorted degrees. We further develop a masked reprojection module across 2D and 3D latent space to bridge the gap between pre-trained 2D backbone and 3D point-wise features. Both quantitative and qualitative experimental re- sults on S3DIS and ScanNet V2 demonstrate the effective- ness of our method.11Code will be available here.

In this paper, we propose a point-based cross-attention transformer named CrossPoints with parametric Global Porous Sampling (GPS) strategy. The attention module is crucial to capture the correlations between different tokens for transformers. Most existing point-based transformers design multi-scale self-attention operations with down-sampled point clouds by the widely-used Farthest Point Sampling (FPS) strategy. However, FPS only generates sub-clouds with holistic structures, which fails to fully exploit the flexibility of points to generate diversified tokens for the attention module. To address this, we design a cross-attention module with parametric GPS and Complementary GPS (C-GPS) strategies to generate series of diversified tokens through controllable parameters. We show that FPS is a degenerated case of GPS, and the network learns more abundant relational information of the structure and geometry when we perform consecutive cross-attention over the tokens generated by GPS as well as C-GPS sampled points. More specifically, we set evenly-sampled points as queries and design our cross-attention layers with GPS and C-GPS sampled points as keys and values. In order to further improve the diversity of tokens, we design a deformable operation over points to adaptively adjust the points according to the input. Extensive experimental results on both shape classification and indoor scene segmentation tasks indicate promising boosts over the recent point cloud transformers. We also conduct ablation studies to show the effectiveness of our proposed cross-attention module with GPS strategy.

Massive high-quality annotated data is required by fully-supervised learning, which is difficult to obtain for image segmentation since the pixel-level annotation is expensive, especially for medical image segmentation tasks that need domain knowledge. As an alternative solution, semi-supervised learning (SSL) can effectively alleviate the dependence on the annotated samples by leveraging abundant unlabeled samples. Among the SSL methods, mean-teacher (MT) is the most popular one. However, in MT, teacher model's weights are completely determined by student model's weights, which will lead to the training bottleneck at the late training stages. Besides, only pixel-wise consistency is applied for unlabeled data, which ignores the category information and is susceptible to noise. In this paper, we propose a bilateral supervision network with bilateral exponential moving average (bilateral-EMA), named BSNet to overcome these issues. On the one hand, both the student and teacher models are trained on labeled data, and then their weights are updated with the bilateral-EMA, and thus the two models can learn from each other. On the other hand, pseudo labels are used to perform bilateral supervision for unlabeled data. Moreover, for enhancing the supervision, we adopt adversarial learning to enforce the network generate more reliable pseudo labels for unlabeled data. We conduct extensive experiments on three datasets to evaluate the proposed BSNet, and results show that BSNet can improve the semi-supervised segmentation performance by a large margin and surpass other state-of-the-art SSL methods.