15 accepted papers
In this paper, we propose BuildingGPT, a novel auto-regressive model for building wireframe reconstruction from point clouds with reinforcement learning.Unlike prior works based on detection or diffusion models, BuildingGPT reformulates the building wireframe reconstruction task into a sequence pred
Multi-view multi-label feature selection aims to identify informative features from heterogeneous views, where each sample is associated with multiple interdependent labels. This problem is particularly important in machine learning involving high-dimensional, multimodal data such as social media, b
While structure-based relocalizers have long strived for point correspondences when establishing or regressing query-map associations, in this paper, we pioneer the use of planar primitives and 3D planar maps for lightweight 6-DoF camera relocalization in structured environments. Planar primitives,
Multi-view multi-label data offers richer perspectives for artificial intelligence, but simultaneously presents significant challenges for feature selection due to the inherent complexity of interrelations among features, views and labels. Attention mechanisms provide an effective way for analyzing
In this paper, we present BWFormer, a novel Transformer-based model for building wireframe reconstruction from airborne LiDAR point cloud. The problem is solved in a ground-up manner here by detecting the building corners in 2D, lifting and connecting them in 3D space afterwards with additional dat…
3D maps assembled from planar primitives are compact and expressive in representing man-made environments. In this paper, we present **NeuralPlane**, a novel approach that explores **neural** fields for multi-view 3D **plane** reconstruction. Our method is centered upon the core idea of distilling g…
"Reconstructing geometry and topology structures from raw unstructured data has always been an important research topic in indoor mapping research. In this paper, we aim to reconstruct the floorplan with a vectorized representation from point clouds. Despite significant advancements achieved in rece…
Music source separation is important for applications such as karaoke and remixing. Much of previous research focuses on estimating short-time Fourier transform (STFT) magnitude and discarding phase information. We observe that, for singing voice separation, phase can make considerable improvement i…
Monaural speech separation is the task of separating target speech from interference in single-channel recordings. Although substantial progress has been made recently in deep learning based speech separation, previous studies usually focus on a single type of interference, either background noise o…
Speaker separation refers to the task of separating a mixture signal comprising two or more speakers. Impressive advances have been made recently in deep learning based talker-independent speaker separation. But such advances are achieved in anechoic conditions. We address talker-independent speaker…
We address speaker-independent co-channel speech separation from the computational auditory scene analysis (CAS A) perspective. Specifically, we decompose the two-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed at the…
We present a neural network based approach to two-channel beamforming. First, single- and cross-channel spectral features are extracted to form a feature map for each utterance. A large neural network that is the concatenation of a convolution neural network (CNN), long short-term memory recurrent n…
Speaker-independent multi-pitch tracking has been a long-standing problem in speech processing. In this study, we extend a recurrent neural network-factorial hidden Markov model (RNN-FHMM) framework and use the utterance-level permutation invariant training (uPIT) criterion for multi-pitch tracking.…
Pitch tracking in noisy speech is a challenging task as temporal and spectral patterns of the speech signal are both corrupted. This paper proposes long short-term memory (LSTM) based methods for pitch probability estimation. Two architectures are investigated. The first one is conventional LSTM tha…
A reliable estimate of pitch in noisy speech is crucial for many speech applications. In this paper, we propose to use speaker-dependent (SD) deep neural networks (DNNs) to model the harmonic patterns of each speaker. Specifically, SD-DNNs take spectral features as input and estimate probabilistic p…