How Data Priors and Scene Parameterization Boost 3D Indoor Reconstruction

Problem Statement

Three‑dimensional indoor scene reconstruction aims to recover the geometry and appearance of indoor environments from a set of multi‑view RGB images. Accurate reconstruction is a prerequisite for downstream tasks such as 3D semantic segmentation, object detection, and spatial reasoning in mixed/augmented reality, autonomous navigation, and robotics.

Key Technical Challenges

Data priors : Existing indoor reconstruction pipelines do not fully exploit available cues such as imaging priors (photometric consistency, edge or boundary information), 2‑D depth and surface‑normal estimates, occlusion constraints, and multi‑view correspondence.

Scene parameterization : Common scene representations (e.g., tri‑plane feature grids) are often too coarse for cluttered indoor spaces, while neural implicit surface models struggle to represent multiple objects with high fidelity and efficient density modeling.

Research Contributions

Integration of Multiple Data Priors

The dissertation investigates how different priors can be combined to improve reconstruction quality and generalization.

Chapter 2 fuses an imaging prior (photometric consistency and edge alignment) with a boundary prior that enforces sharp transitions at object borders.

Chapter 3 extracts geometric priors from estimated depth maps and surface normals, and incorporates them as regularization terms in the loss function.

Chapter 6 introduces multi‑view consistency constraints and a 3‑D local smoothness prior that penalizes abrupt density changes in neighboring voxels.

Improved Scene Parameterization

Two complementary approaches are proposed to represent indoor scenes more effectively.

Chapter 4 presents a novel scene representation that extends the tri‑plane concept with additional feature channels and adaptive resolution, allowing finer detail capture in complex indoor layouts.

Chapter 5 focuses on precise density modeling within neural implicit representations. It designs a density‑aware MLP architecture and a loss that directly supervises the predicted volume density, reducing artifacts in multi‑object reconstructions.

Reconstruction Paradigms

Indoor reconstruction from RGB can be categorized into:

Surface reconstruction : Generates explicit geometry such as depth maps, point clouds, meshes, or voxel grids. This provides a physically grounded model of the environment.

View synthesis (novel‑view rendering) : Produces RGB images from unseen viewpoints by leveraging learned appearance priors, emphasizing photometric consistency rather than explicit geometry.

Learning‑Based Reconstruction Methods Evaluated

3D Convolutional Neural Networks (3D CNNs) : Operate on volumetric feature grids to predict occupancy or signed distance fields, benefiting from the integrated priors described above.

Neural Radiance Fields (NeRF) : Represent scenes as continuous volumetric radiance fields parameterized by an MLP; the work augments NeRF with depth, normal, and smoothness priors to improve indoor accuracy.

3D Gaussian Splatting (3DGS) : Models the scene as a set of anisotropic Gaussian primitives; the dissertation incorporates multi‑view consistency and density regularization to handle cluttered indoor scenes.

Typical Training Pipeline

# Pseudocode for a prior‑augmented reconstruction loss
loss = photometric_loss(images, rendered_images)
loss += lambda_boundary * boundary_loss(depth, edges)
loss += lambda_depth * depth_prior_loss(pred_depth, gt_depth_estimate)
loss += lambda_normal * normal_consistency_loss(pred_normals, gt_normals)
loss += lambda_smooth * smoothness_loss(density_grid)
optimize(loss)

Here, lambda_* are weighting coefficients that balance each prior according to the dataset characteristics.

Impact

By systematically integrating complementary data priors and proposing a more expressive scene parameterization, the proposed methods achieve higher geometric accuracy and visual fidelity on indoor benchmarks, while maintaining better generalization to unseen environments.