arXiv subjects

Purva Kulkarni

Publications and source records attributed to Purva Kulkarni.

3 recordsLinked to original sources

arXiv2025

A Comprehensive Guide to Mesh Simplification using Edge Collapse

Mesh simplification is the process of reducing the number of vertices, edges and triangles in a three-dimensional (3D) mesh while preserving the overall shape and salient features of the mesh. A popular strategy for this is edge collapse, where an edge connecting two vertices is merged into a single vertex. The edge to collapse is chosen based on a cost function that estimates the error introduced by this collapse. This paper presents a comprehensive, implementation-oriented guide to edge collapse for practitioners and researchers seeking both theoretical grounding and practical insight. We review and derive the underlying mathematics and provide reference implementations for foundational cost functions including Quadric Error Metrics (QEM) and Lindstrom-Turk's geometric criteria. We also explain the mathematics behind attribute-aware edge collapse in QEM variants and Hoppe's energy-based method used in progressive meshes. In addition to cost functions, we outline the complete edge collapse algorithm, including the specific sequence of operations and the data structures that are commonly used. To create a robust system, we also cover the necessary programmatic safeguards that prevent issues like mesh degeneracies, inverted normals, and improper handling of boundary conditions. The goal of this work is not only to consolidate established methods but also to bridge the gap between theory and practice, offering a clear, step-by-step guide for implementing mesh simplification pipelines based on edge collapse.

cs.CG↗

arXiv2020

Converting the Point of View of Messages Spoken to Virtual Assistants

Virtual Assistants can be quite literal at times. If the user says "tell Bob I love him," most virtual assistants will extract the message "I love him" and send it to the user's contact named Bob, rather than properly converting the message to "I love you." We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.

cs.CL↗

arXiv2019

Deep neural networks for emotion recognition combining audio and transcripts

In this paper, we propose to improve emotion recognition by combining acoustic information and conversation transcripts. On the one hand, an LSTM network was used to detect emotion from acoustic features like f0, shimmer, jitter, MFCC, etc. On the other hand, a multi-resolution CNN was used to detect emotion from word sequences. This CNN consists of several parallel convolutions with different kernel sizes to exploit contextual information at different levels. A temporal pooling layer aggregates the hidden representations of different words into a unique sequence level embedding, from which we computed the emotion posteriors. We optimized a weighted sum of classification and verification losses. The verification loss tries to bring embeddings from the same emotions closer while separating embeddings from different emotions. We also compared our CNN with state-of-the-art text-based hand-crafted features (e-vector). We evaluated our approach on the USC-IEMOCAP dataset as well as the dataset consisting of US English telephone speech. In the former, we used human-annotated transcripts while in the latter, we used ASR transcripts. The results showed fusing audio and transcript information improved unweighted accuracy by relative 24% for IEMOCAP and relative 3.4% for the telephone data compared to a single acoustic system.

eess.AS↗