PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion

Panoptic lifting is an effective technique to address the 3D panoptic segmentation task by unprojecting 2D panoptic segmentations from multi-views to 3D scene. However, the quality of its results largely depends on the 2D segmentations, which could be noisy and error-prone, so its performance often drops significantly for complex scenes. In this work, we design a new pipeline coined PCF-Lift based on our Probabilis-tic Contrastive Fusion (PCF) to learn and embed probabilistic features throughout our pipeline to actively consider inaccurate segmentations and inconsistent instance IDs. Technical-wise, we first model the probabilistic feature embeddings through multivariate Gaussian distributions. To fuse the probabilistic features, we incorporate the probability product kernel into the contrastive loss formulation and design a cross-view constraint to enhance the feature consistency across different views. For the inference, we introduce a new probabilistic clustering method to effectively associate prototype features with the underlying 3D object instances for the generation of consistent panoptic segmentation results. Further, we provide a theoretical analysis to justify the superiority of the proposed probabilistic solution. By conducting extensive experiments, our PCF-lift not only significantly outperforms the state-of-the-art methods on widely used benchmarks including the ScanNet dataset and the challenging Messy Room dataset (4.4% improvement of scene-level PQ), but also demonstrates strong robustness when incorporating various 2D segmentation models or different levels of hand-crafted noise.

Semi-signed neural fitting for surface reconstruction from unoriented point clouds

Reconstructing 3D geometry from \emph{unoriented} point clouds can benefit many downstream tasks. Recent methods mostly adopt a neural shape representation with a neural network to represent a signed distance field and fit the point cloud with an unsigned supervision. However, we observe that using unsigned supervision may cause severe ambiguities and often leads to \emph{unexpected} failures such as generating undesired surfaces in free space when reconstructing complex structures and struggle with reconstructing accurate surfaces. To reconstruct a better signed distance field, we propose semi-signed neural fitting (SSN-Fitting), which consists of a semi-signed supervision and a loss-based region sampling strategy. Our key insight is that signed supervision is more informative and regions that are obviously outside the object can be easily determined. Meanwhile, a novel importance sampling is proposed to accelerate the optimization and better reconstruct the fine details. Specifically, we voxelize and partition the object space into \emph{sign-known} and \emph{sign-uncertain} regions, in which different supervisions are applied. Also, we adaptively adjust the sampling rate of each voxel according to the tracked reconstruction loss, so that the network can focus more on the complex under-fitting regions. We conduct extensive experiments to demonstrate that SSN-Fitting achieves state-of-the-art performance under different settings on multiple datasets, including clean, density-varying, and noisy data.

AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds

This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points. By analyzing the step of weighted least squares surface fitting, we find that it is hard to determine the polynomial order of the fitting surface and the fitting surface is sensitive to outliers. To address these problems, we propose a simple yet effective solution that adds an additional offset prediction to improve the quality of normal estimation. Furthermore, in order to take advantage of points from different neighborhood sizes, a novel Cascaded Scale Aggregation layer is proposed to help the network predict more accurate point-wise offsets and weights. Extensive experiments demonstrate that AdaFit achieves state-of-the-art performance on both the synthetic PCPNet dataset and the real-word SceneNN dataset.