# Early representation of 3D volumetric shape in visual object processing

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $484,307

## Abstract

Project Summary
The goal of this project is to test a novel theoretical framework for understanding how the ventral pathway
subserves object vision. In the standard framework, a series of neural operations on 2D image data through
many intermediate cortical stages, including area V4, leads to high-level perceptual representations, including
representation of object identity, at the final stages of the ventral pathway. However, our preliminary
microelectrode data from a fixating monkey show that many neurons in V4 represent volumetric (volume-
enclosing) 3D shape, not 2D image patterns. These neurons respond to many different 2D images that convey
the same 3D shape with different shape-in-depth cues, including shading, reflection, and refraction. They even
respond preferentially to random dot stereograms that convey 3D volumetric shape with no 2D cues
whatsoever. Moreover, our preliminary results with 2-photon functional imaging in anesthetized monkey V4
show that 3D shape signals are grouped by their similarity, and also group with isomorphic (same outline) 2D
shape signals (which could contribute evidence to corresponding 3D shape inferences). We propose to
capitalize on these preliminary data by demonstrating the prevalence of 3D shape tuning in area V4, analyzing
the 3D shape coding strategies used by these neurons, and measuring how 2D and 3D shape signals are
arranged at a microscopic level across the surface of V4. We expect these results to provide strong evidence
that extraction of 3D shape fragments is a primary goal of V4 processing. This early extraction of 3D shape
information, just two synapses beyond primary visual cortex, would suggest a competing framework for
understanding the ventral pathway, in which the initial goal is to represent 3D physical structure, independent
of the various 2D image cues used to infer it. In this framework, object recognition would be based on
preceding information about 3D physical structure, which would explain why human object recognition is so
robust to image changes, in a way that the best computational vision systems are not. The scientific impact of
this work would be to divert vision experiments toward understanding representation of real world 3D structure
(rather than 2D planar stimuli) and to encourage computational vision scientists to incorporate early 3D shape
processing into the deep convolutional network models that are the current state of the art.

## Key facts

- **NIH application ID:** 10412966
- **Project number:** 5R01EY029420-05
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** CHARLES E CONNOR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $484,307
- **Award type:** 5
- **Project period:** 2018-09-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412966

## Citation

> US National Institutes of Health, RePORTER application 10412966, Early representation of 3D volumetric shape in visual object processing (5R01EY029420-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10412966. Licensed CC0.

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