MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision

Core Concepts

MOHO presents a synthetic-to-real framework for single-view hand-held object reconstruction, overcoming challenges of hand-induced occlusion and object's self-occlusion.

Abstract

MOHO introduces a novel approach to exploit multi-view occlusion-aware supervision from hand-object videos for single-view hand-held object reconstruction. The synthetic pre-training stage involves rendering a large-scale dataset with occlusion-free supervisions to address hand-induced occlusion. In the real-world finetuning stage, MOHO leverages amodal-mask-weighted geometric supervision to mitigate incomplete supervisions caused by hand-occluded views. Domain-consistent occlusion-aware features are incorporated to overcome object's self-occlusion during the entire process. Extensive experiments demonstrate superior results against 3D-supervised methods.

Stats

Synthetic dataset SOMVideo consists of 141,550 scenes captured from 10 views each.
MOHO pre-trained on SOMVideo for 300K iterations and finetuned for another 300K iterations.
MOHO uses an Adam optimizer with learning rates of 10^-3 for pre-training and 4x10^-4 for finetuning.

Quotes

"MOHO gains superior results against 3D-supervised methods by a large margin."
"In contrast, readily accessible raw videos offer a promising training data source."
"MOHO is capable of inferring the shape of the complete object in real world."

Key Insights Distilled From

MOHO

by Chenyangguan... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2310.11696.pdf

Deeper Inquiries

How can MOHO's synthetic-to-real framework be applied in other areas beyond single-view object reconstruction?

MOHO's synthetic-to-real framework can be applied in various other areas beyond single-view object reconstruction. One potential application is in robotics, specifically in robotic manipulation tasks where objects are interacted with by robot hands. By leveraging the multi-view occlusion-aware supervision from hand-object videos, robots can improve their ability to grasp and manipulate objects effectively even when they are partially occluded. This could enhance the autonomy and efficiency of robotic systems in real-world scenarios.
Another area where MOHO's framework could be beneficial is in augmented reality (AR) and virtual reality (VR) applications. By using the synthetic pre-training stage to render occlusion-free supervisions and then finetuning on real-world data, AR/VR systems could better understand hand-object interactions for more realistic and immersive experiences. For example, this approach could improve gesture recognition or virtual object manipulation within AR/VR environments.
Additionally, the domain-consistent occlusion-aware features incorporated into MOHO could also be valuable in medical imaging applications. For instance, this framework could assist radiologists in reconstructing 3D structures from 2D medical images with partial visibility due to overlapping organs or tissues. The enhanced reconstruction capabilities provided by MOHO could lead to more accurate diagnoses and treatment planning.

What potential limitations or biases may arise from relying solely on heavily occluded real-world supervisions?

Relying solely on heavily occluded real-world supervisions for training a model like MOHO may introduce several limitations and biases:

Incomplete Object Representations: The heavy occlusions present in real-world supervisions may result in incomplete representations of objects during training. This limitation can lead to difficulties for the model when inferring complete shapes based on partial observations.

Biased Reconstruction: The model trained on heavily occluded data may develop biases towards specific types of occlusions commonly seen during training. As a result, it might struggle with novel or uncommon forms of obstruction that were not adequately represented during training.

Limited Generalization: Models trained exclusively on heavily occluded data may have limited generalization capabilities outside those specific conditions encountered during training. They might perform poorly when faced with less obstructed views or different types of obstructions not seen before.

4 .Overfitting to Noise: If the dataset used for training contains noisy or inaccurate annotations due to heavy occlusions, the model might overfit to these errors rather than learning true underlying patterns present in the data.

How might incorporating additional sensory modalities enhance the performance of MOHO in complex scenarios?

Incorporating additional sensory modalities alongside visual information can significantly enhance the performance of models like MOHO, especially in complex scenarios:
1 .Depth Information: Adding depth information through sensors like LiDAR or depth cameras can provide crucial geometric cues that complement visual inputs for improved 3D reconstruction accuracy.
2 .Tactile Feedback: Integrating tactile sensors into the system would enable capturing haptic feedback during interactions between hands and objects.This tactile information would enrich understanding hand-object interactions leadingto more accurate reconstructions.
3 .Audio Cues: Incorporating audio cues such as sound produced during interactionscan offer contextual information about actions being performed.These cuescan help disambiguate ambiguous scenesandimprove overall scene understanding.
4 .Temporal Data Fusion: Utilizing temporal information from video sequences alongsidestatic imagescan aidin tracking dynamic changesduring interactions.This fusionof temporal dynamicswith spatialinformationwouldenhance robustnessandaccuracyin predictingobject deformationsor movementsover time.
5 .Multi-Modal Fusion: Employing techniques such as multi-modal fusion networksfor integratingdatafrom multiple sourcescould facilitate comprehensiveunderstandingof complexscenes.By combiningvisual,tactile,andaudioinputs,the modelcan leverage complementaryinformationacrossmodalitiesto make more informedpredictionsandinferencesaboutthe environment.
By leveraging these additional sensory modalities,Moho’sperformance incapturinghand-heldobjectreconstructionfromsingleviewimagescouldbe greatly enhanced,enablingmorecomprehensiveandreliableanalysisofcomplexreal-worldeventsandinteractions