  Reconstructing 3D shape and pose of static objects from a single image is an
essential task for various industries, including robotics, augmented reality,
and digital content creation. This can be done by directly predicting 3D shape
in various representations or by retrieving CAD models from a database and
predicting their alignments. Directly predicting 3D shapes often produces
unrealistic, overly smoothed or tessellated shapes. Retrieving CAD models
ensures realistic shapes but requires robust and accurate alignment. Learning
to directly predict CAD model poses from image features is challenging and
inaccurate. Works, such as ROCA, compute poses from predicted normalised object
coordinates which can be more accurate but are susceptible to systematic
failure. SPARC demonstrates that following a ''render-and-compare'' approach
where a network iteratively improves upon its own predictions achieves accurate
alignments. Nevertheless, it performs individual CAD alignment for every object
detected in an image. This approach is slow when applied to many objects as the
time complexity increases linearly with the number of objects and can not learn
inter-object relations. Introducing a new network architecture Multi-SPARC we
learn to perform CAD model alignments for multiple detected objects jointly.
Compared to other single-view methods we achieve state-of-the-art performance
on the challenging real-world dataset ScanNet. By improving the instance
alignment accuracy from 31.8% to 40.3% we perform similar to state-of-the-art
multi-view methods.
