# Accelerating Physical Property Reasoning for Augmented Visual Cognition

arXiv: https://arxiv.org/abs/2511.03126 · HTML: https://mlsanity.com/paper/2511.03126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [Project Aria: A New Tool for Egocentric Multi-Modal AI Research](https://mlsanity.com/paper/2308.13561/) (2023)
- [Physical Property Understanding from Language-Embedded Feature Fields](https://mlsanity.com/paper/2404.04242/) (2024)
- [Augmented Object Intelligence with XR-Objects](https://mlsanity.com/paper/2404.13274/) (2024)
- [How well does CLIP understand texture?](https://mlsanity.com/paper/2203.11449/) (2022)
- [SparseLGS: Sparse View Language Embedded Gaussian Splatting](https://mlsanity.com/paper/2412.02245/) (2024)
- [SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending](https://mlsanity.com/paper/2409.13926/) (2024)

## Built on by (most cited first, 1 of 1)

- [PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image](https://mlsanity.com/paper/2609.18920/) (2026)

## Abstract (arXiv)

This paper introduces sysname, a system that accelerates vision-guided physical property reasoning to enable augmented visual cognition. sysname minimizes the run-time latency of this reasoning pipeline through a combination of both algorithmic and systematic optimizations, including rapid geometric 3D reconstruction, efficient semantic feature fusion, and parallel view encoding. Through these simple yet effective optimizations, sysname reduces the end-to-end latency of this reasoning pipeline from 10--20 minutes to less than 6 seconds. A head-to-head comparison on the ABO dataset shows that sysname achieves this 62.9×--287.2× speedup while not only reaching on-par (and sometimes slightly better) object-level physical property estimation accuracy(e.g. mass), but also demonstrating superior performance in material segmentation and voxel-level inference than two SOTA baselines. We further combine gaze-tracking with sysname to localize the object of interest in cluttered, real-world environments, streamlining the physical property reasoning on smart glasses. The case study with Meta Aria Glasses conducted at an IKEA furniture store demonstrates that sysname achives consistently high performance compared to controlled captures, providing robust property estimations even with fewer views in real-world scenarios.