arXiv subjects

Dingsheng Luo

Publications and source records attributed to Dingsheng Luo.

6 recordsLinked to original sources

arXiv2025

Domain Adaptation-Based Crossmodal Knowledge Distillation for 3D Semantic Segmentation

Semantic segmentation of 3D LiDAR data plays a pivotal role in autonomous driving. Traditional approaches rely on extensive annotated data for point cloud analysis, incurring high costs and time investments. In contrast, realworld image datasets offer abundant availability and substantial scale. To mitigate the burden of annotating 3D LiDAR point clouds, we propose two crossmodal knowledge distillation methods: Unsupervised Domain Adaptation Knowledge Distillation (UDAKD) and Feature and Semantic-based Knowledge Distillation (FSKD). Leveraging readily available spatio-temporally synchronized data from cameras and LiDARs in autonomous driving scenarios, we directly apply a pretrained 2D image model to unlabeled 2D data. Through crossmodal knowledge distillation with known 2D-3D correspondence, we actively align the output of the 3D network with the corresponding points of the 2D network, thereby obviating the necessity for 3D annotations. Our focus is on preserving modality-general information while filtering out modality-specific details during crossmodal distillation. To achieve this, we deploy self-calibrated convolution on 3D point clouds as the foundation of our domain adaptation module. Rigorous experimentation validates the effectiveness of our proposed methods, consistently surpassing the performance of state-of-the-art approaches in the field.

cs.CV↗

arXiv2025

DPGP: A Hybrid 2D-3D Dual Path Potential Ghost Probe Zone Prediction Framework for Safe Autonomous Driving

Modern robots must coexist with humans in dense urban environments. A key challenge is the ghost probe problem, where pedestrians or objects unexpectedly rush into traffic paths. This issue affects both autonomous vehicles and human drivers. Existing works propose vehicle-to-everything (V2X) strategies and non-line-of-sight (NLOS) imaging for ghost probe zone detection. However, most require high computational power or specialized hardware, limiting real-world feasibility. Additionally, many methods do not explicitly address this issue. To tackle this, we propose DPGP, a hybrid 2D-3D fusion framework for ghost probe zone prediction using only a monocular camera during training and inference. With unsupervised depth prediction, we observe ghost probe zones align with depth discontinuities, but different depth representations offer varying robustness. To exploit this, we fuse multiple feature embeddings to improve prediction. To validate our approach, we created a 12K-image dataset annotated with ghost probe zones, carefully sourced and cross-checked for accuracy. Experimental results show our framework outperforms existing methods while remaining cost-effective. To our knowledge, this is the first work extending ghost probe zone prediction beyond vehicles, addressing diverse non-vehicle objects. We will open-source our code and dataset for community benefit.

cs.RO↗

arXiv2025

Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation

Long-horizon embodied planning underpins embodied AI. To accomplish long-horizon tasks, one of the most feasible ways is to decompose abstract instructions into a sequence of actionable steps. Foundation models still face logical errors and hallucinations in long-horizon planning, unless provided with highly relevant examples to the tasks. However, providing highly relevant examples for any random task is unpractical. Therefore, we present ReLEP, a novel framework for Real-time Long-horizon Embodied Planning. ReLEP can complete a wide range of long-horizon tasks without in-context examples by learning implicit logical inference through fine-tuning. The fine-tuned large vision-language model formulates plans as sequences of skill functions. These functions are selected from a carefully designed skill library. ReLEP is also equipped with a Memory module for plan and status recall, and a Robot Configuration module for versatility across robot types. In addition, we propose a data generation pipeline to tackle dataset scarcity. When constructing the dataset, we considered the implicit logical relationships, enabling the model to learn implicit logical relationships and dispel hallucinations. Through comprehensive evaluations across various long-horizon tasks, ReLEP demonstrates high success rates and compliance to execution even on unseen tasks and outperforms state-of-the-art baseline methods.

cs.RO↗

arXiv2025

EHC-MM: Embodied Holistic Control for Mobile Manipulation

Mobile manipulation typically entails the base for mobility, the arm for accurate manipulation, and the camera for perception. The principle of Distant Mobility, Close Grasping(DMCG) is essential for holistic control. We propose Embodied Holistic Control for Mobile Manipulation(EHC-MM) with the embodied function of sig(w): By formulating the DMCG principle as a Quadratic Programming (QP) problem, sig(w) dynamically balances the robot's emphasis between movement and manipulation with the consideration of the robot's state and environment. In addition, we propose the Monitor-Position-Based Servoing (MPBS) with sig(w), enabling the tracking of the target during the operation. This approach enables coordinated control among the robot's base, arm, and camera, enhancing task efficiency. Through extensive simulations and real-world experiments, our approach significantly improves both the success rate and efficiency of mobile manipulation tasks, achieving a 95.6% success rate in real-world scenarios and a 52.8% increase in time efficiency.

cs.RO↗

arXiv2024

GauTOAO: Gaussian-based Task-Oriented Affordance of Objects

When your robot grasps an object using dexterous hands or grippers, it should understand the Task-Oriented Affordances of the Object(TOAO), as different tasks often require attention to specific parts of the object. To address this challenge, we propose GauTOAO, a Gaussian-based framework for Task-Oriented Affordance of Objects, which leverages vision-language models in a zero-shot manner to predict affordance-relevant regions of an object, given a natural language query. Our approach introduces a new paradigm: "static camera, moving object," allowing the robot to better observe and understand the object in hand during manipulation. GauTOAO addresses the limitations of existing methods, which often lack effective spatial grouping, by extracting a comprehensive 3D object mask using DINO features. This mask is then used to conditionally query gaussians, producing a refined semantic distribution over the object for the specified task. This approach results in more accurate TOAO extraction, enhancing the robot's understanding of the object and improving task performance. We validate the effectiveness of GauTOAO through real-world experiments, demonstrating its capability to generalize across various tasks.

cs.RO↗

arXiv2020

Spectral-change enhancement with prior SNR for the hearing impaired

A previous signal processing algorithm that aimed to enhance spectral changes (SCE) over time showed benefit for hearing-impaired (HI) listeners to recognize speech in background noise. In this work, the previous SCE was manipulated to perform on target-dominant segments, rather than treating all frames equally. Instantaneous signal-to-noise ratios (SNRs) were calculated to determine whether the segments should be processed. Initially, the ideal SNR calculated by the knowledge of premixed signals was introduced to the previous SCE algorithm (SCE-iSNR). Speech intelligibility (SI) and clarity preference were measured for 12 HI listeners in steady speech-spectrum noise (SSN) and six-talk speech (STS) maskers, respectively. The results showed the SCE-iSNR algorithm improved SI significantly for both maskers at high signal-to-masker ratios (SMRs) and for STS masker at low SMRs, while processing effect on speech quality was small. Secondly, the estimated SNR obtained from real mixtures was used, resulting in another SCE-eSNR. SI and subjective rating on naturalness and speech quality were tested for 7 HI subjects. The SCE-eSNR algorithm showed improved SI for SSN masker at high SMRs and for STS masker at low SMRs, as well as better naturalness and speech quality for STS masker. The limitations of applying the algorithms are discussed.

eess.AS↗