Institution profile

University of Canterbury

Academic institutionaustralasia · nz
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Research library28linked papers
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Selected work

Representative Papers

Tactile Search: Enhancing Targeting in 3D Space

Sep 05, 2026

Visual search is crucial in daily life, from scanning for relevant information to spotting signs of danger. When sensory channels are overloaded or degraded, cognitive tasks can be supported by crossmodal information representations through vibrotactile cues. We introduce Tactile Search, an approach that uses modulation of frequency and amplitude of vibrations to the hands, for guiding attention to the location of objects in 3D space. We evaluated this approach in a competitive VR game where participants searched for targets using both vision and touch. Across two studies -- an in-the-wild demonstration (n=55) and a controlled laboratory experiment (n=28) -- we found that vibrotactile feedback significantly improved performance and increased user confidence. In the combined haptic condition, performance did not differ across target heights. We further analyzed participants'subjective experiences and search strategies highlighting the benefits of the tactile cues. Our findings suggest that Tactile Search can enhance interaction and provide design considerations for integrating haptic search into interactive systems.

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ResPCC: A Loss-Resilient Neural Point Cloud Codec over Lossy Networks

Aug 12, 2026

This work addresses the severe degradation in feature fidelity and geometric quality experienced by existing learning-based point cloud compression methods under lossy network conditions due to packet loss. To this end, we propose the first end-to-end neural codec framework with intrinsic robustness to packet loss, which adaptively adjusts its encoding strategy based on perceived packet loss rates and recovers corrupted features at the decoder. The core innovations include Conditional Adaptive Latent Modulation (CALM), Spatial-Channel Interleaving (SCI), Mask-aware Graph-based Latent Recovery (MGLR), and Dictionary-Based Refinement (DBR). Evaluated on ShapeNet and SemanticKITTI under packet loss rates ranging from 5% to 30%, our method significantly outperforms current baselines, achieving state-of-the-art performance in both reconstruction fidelity and rate-distortion efficiency.

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Recent publications

Latest Papers

Tactile Search: Enhancing Targeting in 3D Space

Sep 05, 2026

Visual search is crucial in daily life, from scanning for relevant information to spotting signs of danger. When sensory channels are overloaded or degraded, cognitive tasks can be supported by crossmodal information representations through vibrotactile cues. We introduce Tactile Search, an approach that uses modulation of frequency and amplitude of vibrations to the hands, for guiding attention to the location of objects in 3D space. We evaluated this approach in a competitive VR game where participants searched for targets using both vision and touch. Across two studies -- an in-the-wild demonstration (n=55) and a controlled laboratory experiment (n=28) -- we found that vibrotactile feedback significantly improved performance and increased user confidence. In the combined haptic condition, performance did not differ across target heights. We further analyzed participants'subjective experiences and search strategies highlighting the benefits of the tactile cues. Our findings suggest that Tactile Search can enhance interaction and provide design considerations for integrating haptic search into interactive systems.

0 citationsRead paper

ResPCC: A Loss-Resilient Neural Point Cloud Codec over Lossy Networks

Aug 12, 2026

This work addresses the severe degradation in feature fidelity and geometric quality experienced by existing learning-based point cloud compression methods under lossy network conditions due to packet loss. To this end, we propose the first end-to-end neural codec framework with intrinsic robustness to packet loss, which adaptively adjusts its encoding strategy based on perceived packet loss rates and recovers corrupted features at the decoder. The core innovations include Conditional Adaptive Latent Modulation (CALM), Spatial-Channel Interleaving (SCI), Mask-aware Graph-based Latent Recovery (MGLR), and Dictionary-Based Refinement (DBR). Evaluated on ShapeNet and SemanticKITTI under packet loss rates ranging from 5% to 30%, our method significantly outperforms current baselines, achieving state-of-the-art performance in both reconstruction fidelity and rate-distortion efficiency.

0 citationsRead paper