Noureddine RAMDI / MV-SAM3D: entropy-weighted multi-view fusion for 3D object reconstruction

Created Mon, 04 May 2026 10:23:01 +0000 Modified Sat, 23 May 2026 20:41:27 +0000
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github-stars 3d-reconstruction multi-view entropy-weighting pose-optimization python

devinli123/MV-SAM3D

MV-SAM3D tackles a common challenge in 3D reconstruction: how to reduce ambiguity and instability when reconstructing objects from single views. By fusing observations from multiple viewpoints, it aims to produce more accurate, stable geometry and maintain consistency across an entire scene.

what mv-sam3d does: multi-view fusion for 3d object reconstruction

At its core, MV-SAM3D extends the capabilities of SAM 3D Objects by integrating a multi-view reconstruction framework. This framework aggregates information from multiple camera viewpoints to improve the quality of 3D object models. The main technical innovation lies in its entropy-based weighting mechanism that handles fusion in two distinct stages, each controlled by configurable alpha parameters (stage1_entropy_alpha=30.0, stage2_entropy_alpha=30.0, and stage2_visibility_alpha=30.0). These parameters influence how much confidence is placed on observations based on their entropy, effectively weighing more certain data points higher during fusion.

The pipeline supports both single-object and multi-object 3D reconstruction. It processes RGBA PNG masks for foreground segmentation and produces GLB files merging multiple objects for scene-level outputs. Optional pose optimization is available to refine camera poses, which helps align the reconstructed scene more accurately and further boosts consistency.

MV-SAM3D depends on two main external projects: SAM 3D Objects for 3D segmentation and Depth Anything 3 for depth data processing. It includes preprocessing scripts designed to build datasets from raw scene images, preparing inputs for the fusion pipeline.

The implementation is in Python, relying on scientific and imaging libraries with scripts to run inference and pose optimization. The design is modular enough to allow experimentation with entropy weighting parameters and pose optimization toggling.

why mv-sam3d stands out technically: entropy weighting and pose optimization

What distinguishes MV-SAM3D is its principled approach to multi-view fusion using entropy-based weights. Instead of treating all views equally or relying on simple heuristics, it computes entropy measures to estimate the uncertainty of each observation. This uncertainty guides the fusion process, reducing the influence of ambiguous or noisy views.

The two-stage fusion approach is notable: the first stage uses entropy to weight observations, while the second stage adds visibility considerations and further refines the fusion. This layered weighting helps stabilize the geometry and reduce errors that typically arise from occlusions or inconsistent views.

Pose optimization is another key technical feature. Multi-view reconstruction’s quality heavily depends on accurate camera pose estimation. MV-SAM3D offers an optional optimization step that adjusts camera poses to better align the reconstructed scene. While this adds computational overhead and complexity, it can significantly improve scene-level consistency.

The code quality appears solid with clear separation of concerns: preprocessing, entropy computation, fusion, and pose optimization live in dedicated scripts or modules. This structure supports debugging and parameter tuning. The tradeoff is added complexity and dependency on external codebases (SAM 3D Objects and Depth Anything 3), which means users need to set up those environments first.

quick start: running single and multi-object inference

To get started, the repo provides straightforward commands for both single-object and multi-object 3D reconstruction. Environment setup requires following instructions from the dependencies SAM 3D Objects and Depth Anything 3.

Single-object inference example:

python run_inference_weighted.py \
  --input_path ./data/example \
  --mask_prompt stuffed_toy \
  --da3_output ./da3_outputs/example/da3_output.npz

Multi-object inference example with merging and pose optimization:

python run_inference_weighted.py \
  --input_path ./data/desk_objects0 \
  --mask_prompt keyboard,speaker,mug,stuffed_toy \
  --da3_output ./da3_outputs/desk_objects0/da3_output.npz \
  --merge_da3_glb \
  --run_pose_optimization

These commands illustrate the flexibility of the pipeline: you specify input image folders, object prompts for segmentation, and output paths. Additional flags control whether to merge objects into a single GLB file and whether to run pose optimization.

verdict: a solid multi-view fusion framework for 3d reconstruction experiments

MV-SAM3D offers a technically interesting extension to single-view 3D object reconstruction by introducing entropy-weighted multi-view fusion and optional pose optimization. The approach addresses common issues with geometry instability and scene consistency, which are key challenges in reconstructing accurate 3D scenes from images.

While not a plug-and-play solution for all use cases due to its dependencies and complexity, it’s a valuable tool for researchers and developers working on multi-view 3D reconstruction who want to experiment with entropy-based weighting and pose refinement.

The codebase is clean enough to follow and modify, making it a good starting point for extending multi-view fusion techniques or integrating with other 3D vision projects. If your work involves improving 3D object quality from multiple views or aligning scenes more precisely, MV-SAM3D is worth exploring.

→ GitHub Repo: devinli123/MV-SAM3D ⭐ 433 · Python