3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse grid of projected dots. Yet, these sparse LiDARs struggle with scene coverage on limited input views, leaving large gaps in depth information.

In this work, we propose using an alternative class of "blurred" LiDAR that emits a diffuse flash, greatly improving scene coverage but introducing spatial ambiguity from mixed time-of-flight measurements across a wide field of view. To handle these ambiguities, we propose leveraging the complementary strengths of diffuse LiDAR with RGB. We introduce a Gaussian surfel-based rendering framework with a scene-adaptive loss function that dynamically balances RGB and diffuse LiDAR signals. We demonstrate that, surprisingly, diffuse LiDAR can outperform traditional sparse LiDAR, enabling robust 3D scanning with accurate color and geometry estimation in challenging environments.

git clone https://github.com/NikhilBehari/bls3d.git
cd bls3d

conda env create --file bls3d_env.yml
conda activate bls3d

Our modified CUDA rasterizer enables gradient-based optimization using transient loss functions. To recompile and install it:

cd submodules/diff-gaussian-rasterization
python setup.py install && pip install .

To train on a dataset, use the train.py script:

python train.py -s ./datasets/folder $(cat param_files/params.txt)

Sample parameter files for our texture and lighting variation experiments are provided in the param_files directory:

• light_variation.txt
• texture_variation.txt

These parameter files can be modified to fine-tune training for specific datasets.

Once training is complete, the trained model will be saved in the output/ directory. Use the render.py script to render images and reconstruct meshes:

python render.py -m path/to/your/trained/model --img --depth 10