12 citations · 12 across the 9 of their papers we have counts for
9 papers
Sparse Code Uplifting for Efficient 3D Language Gaussian Splatting
Lovre Antonio Budimir, Yushi Guan, Steve Ryhner +2
3D Language Gaussian Splatting (3DLGS) augments 3D Gaussian Splatting with language-aligned visual features for open-vocabulary 3D scene understanding. A core challenge is efficien…
TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models
Yushi Guan, Jeanine Ohene-Agyei, Daniel Kwan +3
To embed domain-specific or specialized knowledge into pre-trained foundation models, fine-tuning using techniques such as parameter efficient fine-tuning (e.g. LoRA) is a common p…
MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry
Leo Kaixuan Cheng, Abdus Shaikh, Ruofan Liang +3
Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their…
ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
Sankeerth Durvasula, Sharanshangar Muhunthan, Zain Moustafa +7
3D Gaussian Splatting (3DGS) is a state-of-art technique to model real-world scenes with high quality and real-time rendering. Typically, a higher quality representation can be ach…
Squeeze3D: Extreme Neural Compression with Latent Space Bridging
Rishit Dagli, Yushi Guan, Sankeerth Durvasula +2
We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained encoders and decoders to compress 3D data at extremely high compressi…
INRet: A General Framework for Accurate Retrieval of INRs for Shapes
Yushi Guan, Daniel Kwan, Ruofan Liang +4
Implicit neural representations (INRs) have become an important method for encoding various data types, such as 3D objects or scenes, images, and videos. They have proven to be par…
ACS: Concurrent Kernel Execution on Irregular, Input-Dependent Computational Graphs
Sankeerth Durvasula, Adrian Zhao, Raymond Kiguru +3
GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines…
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception
Sankeerth Durvasula, Yushi Guan, Nandita Vijaykumar
Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., micro…
SPIDR: SDF-based Neural Point Fields for Illumination and Deformation
Ruofan Liang, Jiahao Zhang, Haoda Li +3
Neural radiance fields (NeRFs) have recently emerged as a promising approach for 3D reconstruction and novel view synthesis. However, NeRF-based methods encode shape, reflectance,…