# Physically Guided Visual Mass Estimation from a Single RGB Image

arXiv: https://arxiv.org/abs/2601.20303 · HTML: https://mlsanity.com/paper/2601.20303/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://mlsanity.com/paper/2312.02145/) (2023)
- [Long-CLIP: Unlocking the Long-Text Capability of CLIP](https://mlsanity.com/paper/2403.15378/) (2024)
- [ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding](https://mlsanity.com/paper/2305.08275/) (2023)
- [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://mlsanity.com/paper/2201.07436/) (2022)
- [Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2409.09788/) (2024)
- [Physical Property Understanding from Language-Embedded Feature Fields](https://mlsanity.com/paper/2404.04242/) (2024)
- [PUGS: Zero-shot Physical Understanding with Gaussian Splatting](https://mlsanity.com/paper/2502.12231/) (2025)
- [PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability](https://mlsanity.com/paper/2503.08481/) (2025)
- [Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection](https://mlsanity.com/paper/2503.03562/) (2025)
- [Feel the Force: Contact-Driven Learning from Humans](https://mlsanity.com/paper/2506.01944/) (2025)
- [Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs](https://mlsanity.com/paper/2506.01734/) (2025)
- [Task-oriented Robotic Manipulation with Vision Language Models](https://mlsanity.com/paper/2410.15863/) (2024)
- [Can I lift it? Humanoid robot reasoning about the feasibility of lifting a heavy box with unknown physical properties](https://mlsanity.com/paper/2008.03801/) (2020)
- [GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructions](https://mlsanity.com/paper/2503.16013/) (2025)
- [Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning](https://mlsanity.com/paper/2507.05029/) (2025)

## Built on by (most cited first, 2 of 2)

- [PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image](https://mlsanity.com/paper/2609.18920/) (2026)
- [Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation](https://mlsanity.com/paper/2610.17651/) (2026)

## Abstract (arXiv)

Estimating object mass from visual input is challenging because mass depends jointly on geometric volume and material-dependent density, neither of which is directly observable from RGB appearance. Consequently, mass prediction from pixels is ill-posed and therefore benefits from physically meaningful representations to constrain the space of plausible solutions. We propose a physically structured framework for single-image mass estimation that addresses this ambiguity by aligning visual cues with the physical factors governing mass. From a single RGB image, we recover object-centric three-dimensional geometry via monocular depth estimation to inform volume and extract coarse material semantics using a vision-language model to guide density-related reasoning. These geometry, semantic, and appearance representations are fused through an instance-adaptive gating mechanism, and two physically guided latent factors (volume- and density-related) are predicted through separate regression heads under mass-only supervision. Experiments on image2mass and ABO-500 show that the proposed method consistently outperforms state-of-the-art methods.