Large 3D shape repositories are rapidly expanding, driven by advances in generative modeling, making efficient shape retrieval increasingly important for authoring tools. While text queries capture high-level semantics, they often fail to convey precise geometric details. 3D sketches provide a more expressive means of representing shape geometry, and recent AR/VR developments have made sketch-based retrieval practical. However, existing 3D sketch datasets face three major limitations: (1) reliance on quad meshes or voxel hulls, which often fail on complex or non-manifold shapes; (2) use of fixed-size point clouds that discard stroke connectivity and limit geometric fidelity; and (3) dependence on expensive curve-based or multi-view rendering pipelines, which hinder large-scale data generation. Limited point cloud representations also fail to capture sketch connectivity and topology when used to train retrieval models. To address these challenges, we propose Sketch3R, a scalable framework that converts arbitrary 3D meshes into human-like VR sketches using a graph-based representation that preserves stroke connectivity and adapts to sketch complexity. Leveraging this representation, Sketch3R employs a lightweight graph-attention Siamese network for efficient and accurate sketch-to-shape retrieval. Experiments demonstrate that our method outperforms prior approaches in both accuracy and speed, while robustly handling 3D shapes across diverse topologies.

We present SketchTo3DGen, a novel system for rapid 3D content ideation on a VR headset. SketchTo3DGen combines freehand 3D sketching and audio descriptions to generate photo-realistic 3D assets on-the-fly. Running on a Meta Quest headset with a Unity application, our system leverages remote GPU-accelerated services for AI-driven content creation using intuitive inputs. The user can draw a 3D sketch in mid-air and describe the intended asset verbally; our pipeline transcribes and normalizes the speech into a text prompt, selects informative viewpoints of the 3D VR sketch, generates corresponding images via a state-of-the-art text-to-image model, and finally reconstructs a 3D mesh using an image-to-3D generator. The entire workflow is experienced in VR with minimal interface elements. We describe the design motivations, technical pipeline, and user interaction details of SketchTo3DGen. This VR in-headset pipeline, using intuitive inputs in the form of hand-drawn 3D VR sketch and speech, streamlines 3D modeling, accelerating the generation of articulation ready 3D assets.

Software practitioners use various methods in Requirements Engineering (RE) to elicit, analyze, and specify the requirements of enterprise products. The methods impact the final product characteristics and influence product delivery. Ad-hoc usage of the methods by software practitioners can lead to inconsistency and ambiguity in the product. With the notable rise in enterprise products, games, and so forth across various domains, Virtual Reality (VR) has become an essential technology for the future. The methods adopted for RE for developing VR products requires a detailed study. This article presents a mapping study on RE methods prescribed and used for developing VR applications including requirements elicitation, requirements analysis, and requirements specification. Our study provides insights into the use of such methods in the VR community and suggests using specific RE methods in various fields of interest. We also discuss future directions in RE for VR products.

Research in hardware and software support for Virtual Reality (VR) has significantly increased over the last decade. Given the software platform fragmentation and hardware volatility, there is an apparent disconnect among practitioners while building applications in the VR domain. This paper proposes a role-based model template as a meta-model to specify the bare minimum VR software system. We conducted a grounded-theory-based qualitative study on prevailing and phased-out VR SDKs and standards to propose this meta-model. This model template can help VR practitioners build open-source tools to develop, design, and test VR software systems.

Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Since customers typically tend to buy sets of items (i.e., itemsets) together, several research efforts have been undertaken towards facilitating itemset placement in retail stores for improving retailer revenue. However, they fail to consider that the time-period of expiry can vary across items i.e., some items expire sooner than others. This leads to loss of opportunity towards improving retailer revenue. Hence, we propose PEAR, which is a Product Expiry-Aware and Revenue-conscious itemset placement scheme for improving retailer revenue. Our key contributions are three-fold. First, we introduce the problem of addressing retail itemset placement when the items can be associated with different time-periods of expiry. Second, we propose the expiry-aware PEAR scheme for efficiently identifying and placing high-revenue itemsets for improving retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate that PEAR is indeed effective in improving retailer revenue w.r.t. a reference scheme.