arXiv subjects

Mallesham Dasari

Publications and source records attributed to Mallesham Dasari.

7 recordsLinked to original sources

arXiv2026

Understanding Organizational Strategies Across Multimodal Artifacts in Immersive Computational Notebooks

Immersive Computational Notebooks (ICoN) extend traditional notebook environments into immersive spaces, enabling analysts to interact with multimodal artifacts, including code, narratives, data tables, and visualizations. By integrating multimodal artifacts into a single immersive workspace, ICoN enables analysts to transition between analytical tasks seamlessly. Meanwhile, understanding organizational strategies is critical for designing effective interactions to further support analysts. However, prior research on immersive computational notebooks has primarily examined organizational strategies centered on single-modality artifacts. Systematic investigations of how analysts spatially organize the complex relationships among multimodal artifacts in a single immersive workspace remain underexplored. To address this gap, we conducted a user study to examine organizational strategies for multimodal artifacts in immersive computational notebooks. Our findings show that participants predominantly adopted depth-based layouts, and their spatial organization was largely structured around cell-based artifacts.

cs.HC↗

arXiv2026

N4MC: Neural 4D Mesh Compression

We present N4MC, the first 4D neural compression framework to efficiently compress time-varying mesh sequences by exploiting their temporal redundancy. Unlike prior neural mesh compression methods that treat each mesh frame independently, N4MC takes inspiration from inter-frame compression in 2D video codecs, and learns motion compensation in long mesh sequences. Specifically, N4MC converts consecutive irregular mesh frames into regular 4D tensors to provide a uniform and compact representation. These tensors are then condensed using an auto-decoder, which captures both spatial and temporal correlations for redundancy removal. To enhance temporal coherence, we introduce a transformer-based interpolation model that predicts intermediate mesh frames conditioned on latent embeddings derived from tracked volume centers, eliminating motion ambiguities. Extensive evaluations show that N4MC outperforms state-of-the-art in rate-distortion performance, while enabling real-time decoding of 4D mesh sequences. The implementation of our method is available at: https://github.com/frozzzen3/N4MC.

cs.CV↗

arXiv2025

Grasp-HGN: Grasping the Unexpected

For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. To advance next-generation prosthetic hand control design, it is crucial to address current shortcomings in robustness to out of lab artifacts, and generalizability to new environments. Due to the fixed number of object to interact with in existing datasets, contrasted with the virtually infinite variety of objects encountered in the real world, current grasp models perform poorly on unseen objects, negatively affecting users' independence and quality of life. To address this: (i) we define semantic projection, the ability of a model to generalize to unseen object types and show that conventional models like YOLO, despite 80% training accuracy, drop to 15% on unseen objects. (ii) we propose Grasp-LLaVA, a Grasp Vision Language Model enabling human-like reasoning to infer the suitable grasp type estimate based on the object's physical characteristics resulting in a significant 50.2% accuracy over unseen object types compared to 36.7% accuracy of an SOTA grasp estimation model. Lastly, to bridge the performance-latency gap, we propose Hybrid Grasp Network (HGN), an edge-cloud deployment infrastructure enabling fast grasp estimation on edge and accurate cloud inference as a fail-safe, effectively expanding the latency vs. accuracy Pareto. HGN with confidence calibration (DC) enables dynamic switching between edge and cloud models, improving semantic projection accuracy by 5.6% (to 42.3%) with 3.5x speedup over the unseen object types. Over a real-world sample mix, it reaches 86% average accuracy (12.2% gain over edge-only), and 2.2x faster inference than Grasp-LLaVA alone.

cs.RO↗

arXiv2025

SVD: Spatial Video Dataset

Stereoscopic video has long been the subject of research due to its capacity to deliver immersive three-dimensional content across a wide range of applications, from virtual and augmented reality to advanced human-computer interaction. The dual-view format inherently provides binocular disparity cues that enhance depth perception and realism, making it indispensable for fields such as telepresence, 3D mapping, and robotic vision. Until recently, however, end-to-end pipelines for capturing, encoding, and viewing high-quality 3D video were neither widely accessible nor optimized for consumer-grade devices. Today's smartphones, such as the iPhone Pro, and modern Head-Mounted Displays (HMDs), like the Apple Vision Pro (AVP), offer built-in support for stereoscopic video capture, hardware-accelerated encoding, and seamless playback on devices like the Apple Vision Pro and Meta Quest 3, requiring minimal user intervention. Apple refers to this streamlined workflow as spatial video. Making the full stereoscopic video process available to everyone has made new applications possible. Despite these advances, there remains a notable absence of publicly available datasets that include the complete spatial video pipeline. In this paper, we introduce SVD, a spatial video dataset comprising 300 five-second video sequences, 150 captured using an iPhone Pro and 150 with an AVP. Additionally, 10 longer videos with a minimum duration of 2 minutes have been recorded. The SVD dataset is publicly released under an open-access license to facilitate research in codec performance evaluation, subjective and objective quality of experience (QoE) assessment, depth-based computer vision, stereoscopic video streaming, and other emerging 3D applications such as neural rendering and volumetric capture. Link to the dataset: https://cd-athena.github.io/SVD/

cs.MM↗

arXiv2024

CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality

High-quality environment lighting is essential for creating immersive mobile augmented reality (AR) experiences. However, achieving visually coherent estimation for mobile AR is challenging due to several key limitations in AR device sensing capabilities, including low camera FoV and limited pixel dynamic ranges. Recent advancements in generative AI, which can generate high-quality images from different types of prompts, including texts and images, present a potential solution for high-quality lighting estimation. Still, to effectively use generative image diffusion models, we must address two key limitations of content quality and slow inference. In this work, we design and implement a generative lighting estimation system called CleAR that can produce high-quality, diverse environment maps in the format of 360{\deg} HDR images. Specifically, we design a two-step generation pipeline guided by AR environment context data to ensure the output aligns with the physical environment's visual context and color appearance. To improve the estimation robustness under different lighting conditions, we design a real-time refinement component to adjust lighting estimation results on AR devices. Through a combination of quantitative and qualitative evaluations, we show that CleAR outperforms state-of-the-art lighting estimation methods on both estimation accuracy, latency, and robustness, and is rated by 31 participants as producing better renderings for most virtual objects. For example, CleAR achieves 51% to 56% accuracy improvement on virtual object renderings across objects of three distinctive types of materials and reflective properties. CleAR produces lighting estimates of comparable or better quality in just 3.2 seconds -- over 110X faster than state-of-the-art methods.

cs.CV↗

arXiv2024

Volume Tracking Based Reference Mesh Extraction for Time-Varying Mesh Compression

Time-Varying meshes (TVMs), characterized by their varying connectivity and number of vertices, hold significant potential in immersive media and other various applications. However, their practical utilization is challenging due to their time-varying features and large file sizes. Creating a reference mesh that contains the most essential information is a promising approach to utilizing shared information within TVMs to reduce storage and transmission costs. We propose a novel method that employs volume tracking to extract reference meshes. First, we adopt as-rigid-as-possible (ARAP) volume tracking on TVMs to get the volume centers for each mesh. Then, we use multidimensional scaling (MDS) to get reference centers that ensure the reference mesh avoids self-contact regions. Finally, we map the vertices of the meshes to reference centers and extract the reference mesh. Our approach offers a feasible solution for extracting reference meshes that can serve multiple purposes such as establishing surface correspondence, deforming the reference mesh to different shapes for I-frame based mesh compression, or defining the global shape of the TVMs.

cs.MM↗

arXiv2022

RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion

The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments - a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously. In this work, we take an important step towards this goal by introducing the notion of dual-layer diversity to the problem of sensor fusion in multi-agent tracking. We demonstrate that the fusion of complementary tracking modalities, - passive/relative (e.g., visual odometry) and active/absolute tracking (e.g., infrastructure-assisted RF localization) offer a key first layer of diversity that brings scalability while the second layer of diversity lies in the methodology of fusion, where we bring together the complementary strengths of algorithmic (for robustness) and data-driven (for accuracy) approaches. RoVaR is an embodiment of such a dual-layer diversity approach that intelligently attends to cross-modal information using algorithmic and data-driven techniques that jointly share the burden of accurately tracking multiple agents in the wild. Extensive evaluations reveal RoVaR's multi-dimensional benefits in terms of tracking accuracy (median of 15cm), robustness (in unseen environments), light weight (runs in real-time on mobile platforms such as Jetson Nano/TX2), to enable practical multi-agent immersive applications in everyday environments.

cs.RO↗