3x3x3 Resnet Basic Block

Pytorch Dataset for a single scene. getitem loads individual frames

class to hold a truncated signed distance function (TSDF) Holds the TSDF volume along with meta data like voxel size and origin required to

Applies a projected skip connection from the encoder to the decoder When condition is False this is a standard projected skip connection (co

Pytorch Dataset for a multiple scenes getitem loads a sequence of frames from a scene along with the corresponding TSDF for the scene

Accumulates depth maps into a TSDF using TSDF fusion

Does tensorboard logging + has a MeshWriter for saving example meshes throughout training

Remove once https://github.com/rbgirshick/yacs/issues/19 is merged

Predicts voxel color

3D network to refine feature volumes

Converts feature pyrimid to singe feature map (from Detectron2)

Saves mesh to logdir during training

Module that contains all the 2D output heads Features extracted by the 2D network are passed to this to produce intermeditate per-frame

OpenGL mesh renderer Used to render depthmaps from a mesh for 2d evaluation

Predicts voxel semantic segmentation

2D image semantic segmentation

Main head that regresses the TSDF

Module that contains all the 3D output heads Features extracted by the 3D network are passed to this to produce the final outputs. Each

Network architecture implementing ATLAS (https://arxiv.org/pdf/2003.10432.pdf)

Apply a list of transforms sequentially

Take data out of TSDF data structure so we can collate into a batch

Convert instance images to semseg images. Also map to benchmark classes

Convert intrinsics and extrinsics matrices to a single projection matrix

Apply a random 3x4 linear transform to the world coordinate system.

Resize everything to given size. Intrinsics are assumed to refer to image prior to resize. After resize everything (ex: depth) should have t

Convert to torch tensors

See transform_space

Create a RGB colormap for a semseg image